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Hire remote Product Researcher

Discover developers that match your project requirements.

No exact match for this specialty yet — here are related experts from our network.

Aditya P.
Available Now
Verified in SoftDoesAditya P.
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Andrea M.
Available Now
Verified in SoftDoesAndrea M.
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrii V.
Available Now
Verified in SoftDoesAndrii V.
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Boris S.
Available Now
Verified in SoftDoesBoris S.
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Eugene M.
Available Now
Verified in SoftDoesEugene M.
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Hripsime S.
Available Now
Verified in SoftDoesHripsime S.
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Mario J.
Available Now
Verified in SoftDoesMario J.
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Raphael O.
Available Now
Verified in SoftDoesRaphael O.
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Santiago G.
Available Now
Verified in SoftDoesSantiago G.
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Thierry M.
Available Now
Verified in SoftDoesThierry M.
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thomas S.
Available Now
Verified in SoftDoesThomas S.
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Tzechung K.
Available Now
Verified in SoftDoesTzechung K.
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

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What our Product Researchers can build

Not sure which engagement model fits?

SoftDoes takes full ownership of delivery, combining project management, engineering, design, and QA into one accountable team focused on successful outcomes.

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RIGHT expert, FASTER

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How to hire a Product Researcher

01
BROWSE PROFILESRIGHT NOW

Fill out a short form and see who's on the bench. Real profiles, verified histories.

02
Interview1-3 DAYS

Tell us what you need. We propose two or three candidates from the bench; you interview them directly.

03
OnboardWEEK ONE

Your engineer starts on your project. Contract, payments, and the guarantee run through us.

US VS. THE DATABASE

Time to Start
Talent Quality
Technical Vetting
Flexibility
Operational Overhead
Cost Efficiency
cursor
<SoftDoes>
Time to Start
1-2 weeks
Talent Quality
Senior-only engineers
Technical Vetting
Multi-stage screening
Flexibility
Scale up or down anytime
Operational Overhead
As managed as you want
Cost Efficiency
Competitive, fee-free
Talent Marketplaces
Time to Start
1-3 months
Talent Quality
Mixed experience levels
Technical Vetting
One screen, then gone
Flexibility
Contract restrictions
Operational Overhead
Partially managed
Cost Efficiency
Agency markup
In-House Hiring
Time to Start
2-6 months
Talent Quality
Depends on market
Technical Vetting
Internal responsibility
Flexibility
Long-term commitment
Operational Overhead
Fully internal
Cost Efficiency
Highest total cost

Frequently Asked Questions

Everything you need to know about deploying, scaling, and securing your neural agents with SoftDoes. Can’t find an answer?

How long does it take to hire a Product Researcher through SoftDoes?

For most companies, filling a senior product researcher position through traditional hiring takes anywhere from four to eight weeks of active search, not counting the time lost to misaligned candidates or failed offers. SoftDoes maintains a network of pre vetted talent, which means you can typically be matched with a qualified researcher and begin onboarding within two to three weeks. Freelance product researchers can sometimes be engaged within 72 hours for urgent needs. Add about 30 days of ramp time for full productivity, though quick win projects can start delivering value almost immediately.

What does it cost to hire a Product Researcher?

The pay range for a product researcher in a B2B software context varies based on seniority, location, and engagement model. In the US, typical compensation for mid to senior level roles falls in the range you would expect for specialized analytical and strategic positions. Markets like San Francisco tend to sit at the higher end of that range. Remote, nearshore, or fractional engagement models can adjust cost depending on the scope and length of engagement. Beyond salary, factor in tooling, participant sourcing, privacy and compliance overhead, and travel, especially in regulated industries. SoftDoes can help you determine the right model to balance cost with the level of expertise and access your company needs.

What engagement models are available (dedicated hire, pod, contract)?

SoftDoes offers several flexible models. You can bring on a single dedicated product researcher embedded into your team for focused, ongoing market research or user research. For companies with multiple product lines or complex research requirements, a research pod (a team of researcher, analyst, and operations support) provides broader coverage. Contract or fractional models work well for specific discovery phases or discrete projects. You can also transition between models, moving from a contract engagement to a full hire or scaling from a single researcher to a pod as your growth demands.

How do you ensure time zone alignment with a Product Researcher?

SoftDoes focuses on North American talent, ensuring overlap with US and Canadian business hours. For distributed teams, we establish core working hours, clear communication norms, and async documentation practices. Shared dashboards and regular check ins keep research aligned with product development cycles regardless of where team members are located. This approach ensures that collaboration across time zones does not slow down decision making or create bottlenecks for your cross functional teams.

How does SoftDoes technically vet a Product Researcher?

SoftDoes uses a multi stage screening process designed to surface real capability, not just credentials. This includes skills testing across both qualitative and quantitative methods, case study or portfolio review focused on decisions made during the research process rather than polished decks, domain specific interviews, and reference checks. We also verify tool proficiency (analytics platforms, survey tools, usability testing software), compliance and industry experience depending on your vertical, and the candidate's ability to handle ambiguity and stakeholder management. The goal is to confirm that every researcher we recommend has the research judgment, analytical skills, and communication ability to deliver results from day one.

What happens if the Product Researcher isn't the right fit, or I need to scale up or down?

SoftDoes offers replacement guarantee clauses so you are never stuck with an underperforming hire. If the researcher is not the right fit, we will work with you to identify the gap and provide a replacement quickly. You also have the flexibility to scale your engagement up or down, adding or subtracting resources as your project needs change. If your needs evolve, we can transition your engagement model from contract to full hire, or from a single researcher to a full pod. Exit planning and project wrap up are handled professionally so there is no disruption to your team or ongoing research.

How to Hire a Product Researcher

Most companies burn weeks, sometimes months, searching for a product researcher who can actually move the needle. They end up with candidates who produce thick reports but zero strategic insight, or they settle for generalists who lack the analytical skills to validate product decisions. This guide walks you through exactly how to define the role, prepare internally, source and vet candidates, and make a confident hiring decision that connects research directly to business outcomes.

What a Product Researcher Really Does and Why It Matters

The Evolving Role: What a Product Researcher Actually Does

A product researcher's core mission is to reduce uncertainty in product decisions by gathering evidence about users, markets, competitors, and technology. This is not the same as a UX researcher who focuses primarily on usability and design interaction, or a product manager who decides what gets built. The product researcher spans strategic, market, and user evidence, synthesizing data to inform business strategies and drive product innovation.

In practice, the daily work looks like this:

  • Planning and executing mixed method research: User interviews, usability testing, field studies, survey design, and quantitative analysis (cohort analyses, behavioral data) to detect user needs or friction points. Surveys remain a key method for quantitative research studies, while A/B testing is a common methodology for validating hypotheses.
  • Competitive and market analysis: Benchmarking competitor products, mapping feature sets, pricing intelligence, and trend forecasting. Competitor analysis includes pricing strategy and keyword gaps. Tools like Helium 10 are standard for Amazon product research, and platforms like Jungle Scout support e commerce research.
  • Hypothesis formulation and validation: Defining research questions, setting clear product evaluation goals, designing concept tests or prototypes, running experiments. Research judgment is a critical competency here.
  • Synthesizing findings and communicating insights: Product researchers deliver structured reports for actionable insights. They analyze market trends to identify product opportunities. Strong communication skills are required for researchers to share insights effectively with cross functional teams and different audiences.
  • Measuring outcomes and tracking post launch performance: Setting up product health metrics (NPS, churn, engagement), gathering customer reviews and feedback after release, iterating on the product design.
  • Research operations and tooling: Managing participant recruitment, tools and infrastructure (survey platforms, usability labs, data analytics dashboards), and ensuring research ethics and compliance. Ethical and privacy standards awareness is important for protecting user data during research.

The skills a successful candidate needs include qualitative methods, quantitative literacy (proficiency in SQL and R is preferred for data analysis roles), stakeholder management, domain expertise, rapid hypothesis testing, and the ability to balance depth with speed. Experience in product research and analytics is often required. Strong empathy is crucial for uncovering deep user needs, and curiosity, the ability to ask deep questions about user behavior, separates good researchers from great ones.

Why Hiring the Right Product Researcher Is a Strategic Priority

Bringing the right product researcher onto your team is not a nice to have. It directly affects your bottom line:

  • Earlier validation of product ideas: Fewer resources wasted on features or products that miss real user or market needs. This means lower risk of failed launches and better allocation of engineering effort.
  • Faster time to market: Informed decisions earlier in discovery prevent long rework cycles once engineering is engaged. Market research done well accelerates every phase of development.
  • Scalable growth: Gathering insights across product lines allows systematic prioritization, segment understanding, and offering differentiation, especially in B2B and enterprise environments where user research must serve different audiences.
  • Improved compliance and system reliability: In regulated industries (finance, healthcare, education), a skilled market research analyst integrates regulatory impact, security, and privacy expectations into product research, avoiding costly mistakes that can derail an entire product line.

Preparing to Hire the Right Product Researcher

Defining Your Needs Before Opening the Role

Hiring researchers requires defining core competencies early in the process. Before you post the job, leadership should be aligned on the specific problems the researcher must solve.

Project Scope and Requirements

Define project needs before hiring product researchers. Ask: Is the work mainly exploratory discovery (new market, new features), optimization of an existing product, competitive intelligence, or regulatory and compliance research? Specify which products, segments, and platforms (mobile, web, cloud, AI) are in scope. A complex SaaS system in healthcare has very different research demands than a consumer mobile app or an e commerce platform. Setting clear product evaluation goals is essential at this stage.

Team Structure and Engagement Model

Will the researcher report into Product, UX, R&D, or a centralized insights org? Will they be embedded in a cross functional team (design, engineering, PM), or will they support multiple squads from a centralized research function? Embedded models give context and quicker feedback. Centralized models give consistency and shared infrastructure. Growth stage companies tend to favor squads with embedded research to accelerate decision cycles. Your product management function should have a clear view of how research will integrate into existing workflows.

In House vs. Dedicated Remote Talent

Many companies are comfortable hiring senior research roles remotely, especially for qualitative and interview based work. Remote hiring expands your candidate pool and can lower cost, but it requires strong collaboration culture and tooling. Some components (ethnographic studies, in person usability testing) benefit from local presence. Hybrid models work well when operations, participant recruitment, and remote study tools are set up properly. Consider time zones, communication norms, and async documentation when evaluating remote or nearshore options.

Writing a Job Description That Attracts the Right Candidates

Use detailed job descriptions to attract qualified candidates. A vague posting draws vague applicants. A standout job description for a product researcher covers four key elements:

  1. The Mission: What the researcher will actually be solving. Instead of "help product team understand users," be specific: "You will lead discovery for our AI powered fraud detection platform in financial services, uncover regulation driven UX constraints, and validate feature ideas with enterprise clients."
  2. The Stack and Context: What research tools, data sources, and platforms will they use or manage? Include product maturity, domain complexity, user population (enterprise clients, regulated data), and compliance requirements. Mention relevant tools and data analytics expectations.
  3. Team Structure: To whom they report, who they collaborate with (PMs, engineering, UX/design, compliance, data science), whether the position is embedded or centralized, and how many stakeholders or teams they will serve.
  4. Growth and Impact: What is the path forward? Seniority expectations (individual contributor vs. manager vs. strategic partner), what success looks like (metrics impacted, research footprint), and what influence with leadership they will have. Business acumen helps researchers relate findings to company goals and KPIs, so highlight how the role connects to strategy and brand development.
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Finding, Vetting, and Onboarding Your Hire

How to Source and Vet Product Research Talent

Sourcing Strategy

Specialized job boards are essential for sourcing product research candidates. Use talent networks focused on research or product insights roles, UX researcher communities, and research consultancies. Also explore adjacent fields: market research, user experience research, data science, and social sciences backgrounds.

Outbound recruiting matters because many top candidates are not actively looking. Headhunting from companies known for strong product research, especially in regulated or complex domains like fintech, healthtech, or edtech, can unearth candidates with deep domain expertise. Recruiting from diverse backgrounds can enhance the pool of potential candidates. Freelance product researchers can be hired within 72 hours through specialized platforms, and some networks offer access to the top tier of global talent. Consider tapping into our talent network if speed and quality are priorities.

Remote and nearshore options are viable if you are constrained by budget or local candidate supply. Ensure cultural and timezone alignment. Candidates should demonstrate experience with specific marketplaces or industries relevant to your business.

Vetting Beyond the Resume

Effective hiring of researchers requires a structured interview process to compare candidates objectively. Demonstrated research judgment is often valued over formal credentials. Here is what thorough vetting looks like:

  • Technical screening: Test for both qualitative and quantitative methods. Provide a small survey dataset to analyze or a case about a product idea asking "what research would you do, what criteria would you use, what metrics would you track." Data analytics is essential for synthesizing research insights.
  • Real world task: Ask candidates to audit a competitor's product, conduct a mini user interview, or design a concept test. Assess clarity, rigor, and communication of findings. A well scoped brief improves bid quality whether you are hiring full time or freelance.
  • Problem solving interview: Ask scenario based questions: how they handle ambiguous user feedback, conflicting stakeholder demands, or uncertain data. Candidates should be tested on how they handle ambiguity during the research process. Assessing stakeholder management skills helps ensure researchers can translate insights into actionable plans.
  • Portfolio review: Portfolio interviews should focus on decisions made during the research process, not just polished deliverables. Look for a keen eye for detail and evidence of influencing product direction.
  • Domain and compliance awareness: For regulated industries, ask specifically about experience handling privacy, regulation, and data security constraints. This is non negotiable in finance, healthcare, or education.
  • Culture fit: Evaluate collaboration skills, ability to work with cross functional teams, adaptability to fast changes, and ability to influence without formal authority. A self starter mentality is essential, especially in remote or distributed environments.

Onboarding and Retention: The 30/60/90 Day Setup

Researchers must be adaptable to shifting project needs and findings. A structured onboarding plan ensures your new hire ramps up fast and delivers value early:

  • First 30 days: Immerse the researcher in your products, business model, and users. Give access to existing data, past research studies, analytics platforms, and key stakeholders. Assign small "quick wins" research tasks to establish credibility and build relationships across the team.
  • Days 30 to 60: Assign independent research projects. The researcher begins handling cross functional collaboration with UX, engineering, and PM. Set initial metrics such as time to insights delivered and number of recommendations implemented. Explore how they develop their own process for the company's unique context.
  • Days 60 to 90: Help the researcher own recurring research rhythms. Start building a roadmap of research priorities. Create feedback loops with leadership. Begin measuring impact: how many product decisions improved, how many features avoided based on negative findings.

For retention, provide mentorship, opportunities for senior influence, and recognition for outcomes rather than volume of research. Ensure the researcher's work is visible, used, and connected to real business success. Keep the growth path clear and the position valued.

Making the Right Decision

Red Flags and Green Flags When Evaluating Product Research Candidates

Red flags (warning signals during interviews):

  • Vague about process: The candidate cannot clearly explain how they plan research, choose methods, or design experiments. If they cannot walk you through a recent project with specifics, that is a problem.
  • Weak synthesis and communication: Produces reports or deliverables that are purely descriptive without actionable insights. You need someone who can assist leadership in making decisions, not just collect data.
  • Limited quantitative literacy: Cannot analyze metrics, run queries, or interpret analytics dashboards. Analytical skills are non negotiable for this role.
  • Domain ignorance with no curiosity: No understanding of your industry (compliance, regulation, privacy, domain specific user populations) and no interest in learning.

Green flags (indicators of senior level capability):

  • Clear track record of influencing product decisions: Not just producing slides but measurable outcomes tied to marketing, product design, or revenue.
  • Ability to move fast with rigor: Can run discovery research studies in tight timelines without sacrificing validity. This is the mark of extensive experience.
  • Cross functional collaboration skills: Comfortable bridging with PM, product design, engineering, and marketing. Advocates for user experience and consumer insight but remains pragmatic about tradeoffs.
  • Strong domain knowledge or rapid learning ability: Picks up domain constraints (legal, security, regulated data) quickly, plus demonstrates tool fluency and information architecture awareness.

Why Partnering with SoftDoes Gives You an Edge

SoftDoes offers access to carefully vetted senior product research professionals experienced in regulated industries including finance, healthcare, and education. Instead of spending months recruiting from scratch, you get candidates who already understand domain compliance, privacy, and security, and who can deliver insights from day one.

What sets SoftDoes apart:

  • Team delivery model: Rather than isolated freelancers who disappear when projects get complex, SoftDoes provides dedicated teams with built in redundancy, institutional knowledge, and stronger collaboration norms. You can hire a single specialist, a full pod, or scale between models as your needs evolve.
  • Replacement and scaling guarantees: If a researcher is not the right fit, SoftDoes offers replacement guarantees. You can scale up or down without the overhead of traditional hiring.
  • North America focus: Timezone alignment, cultural fit, and compliance expectations are built in for enterprise clients across the US and Canada.
  • Flexible engagement models: From contract based roles for a specific discovery phase to long term embedded talent, SoftDoes matches the engagement to your business needs. Explore our approach to hiring program managers and related roles for a sense of how structured our process is.

Ready to Hire a Product Researcher?

If you are interested in building a product research function that actually drives strategy, the next step is simple. Schedule a discovery call with SoftDoes. We will assess your current research gaps, determine the right engagement model, and match you with senior talent who can start delivering actionable insights fast.

Stop wasting budget on researchers who generate reports nobody reads. Start building a research capability that shapes every product decision.

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