Product Manager – Data Science

Fundamental

. Own and lead the roadmap and product decisions for data science products, from customer discovery through product definition, launch, and iteration .

Posted 10/21/2026full-timeRemoteMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in owning and leading the roadmap for data science products, with a strong focus on MLOps workflows and user experience design. Proficient in data-driven decision-making and collaboration with engineering and data science teams to deliver impactful solutions.

Highest-signal resume keywords

Product OwnershipMLOps WorkflowsData Science ExpertiseCustomer Discovery SkillsCollaboration with Engineers

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceClassificationRegressionTime Series AnalysisPredictive ML WorkflowsUser Experience DesignData-Driven PrioritizationProduct DefinitionIteration

Soft Skills

Strong CollaborationProblem-SolvingCommunication

Industry Keywords

AI AgentsData Science ToolsUser TrustPredictive QualityEnterprise Customer

About the role

Key responsibilities & impact

Own and lead the roadmap and product decisions for data science products, from customer discovery through product definition, launch, and iteration
Work directly with data scientists to understand their needs and usage patterns
Shape AI agents and data science tools across the platform, making common workflows intuitive while preserving flexibility for technical users
Define how users guide agents, inspect their work, and understand results, balancing automation with transparency and control
Own MLOps workflows for managing and monitoring predictive quality of customer models in production
Turn NEXUS capabilities into coherent user experiences with clear product requirements and practical tradeoffs
Define success measures for usability, adoption, and customer outcomes; use data, feedback, and product evidence to guide priorities
Collaborate with engineering, Applied AI/FDEs, product design, and research teams

Requirements

What you’ll need

4+ years of experience owning and shipping ML or data science products with an engineering team, from discovery and roadmap decisions through delivery and iteration
2+ years hands-on expertise in data science, demonstrating a solid grasp of core workflows including classification, regression, and time series analysis
Experience working with structured or tabular data and predictive ML workflows
Experience building agentic products, with practical understanding of their limitations and the interaction patterns that help users trust and control them
Strong customer discovery skills: uncover the problem behind a request, test assumptions and translate learnings into product decisions
Technical fluency and strong collaboration with engineers and applied data scientists, with the ability to turn ambiguity into a clear, deliverable scope
Data-driven prioritization grounded in usage data, evaluation results, and user evidence
Nice to have: earlier client-facing data science experience followed by product ownership with an engineering team
Nice to have: experience building internal data science tools for Applied AI, solutions teams, or technical customer-facing teams
Nice to have: experience in an early-stage AI company with enterprise customer or forward-deployed exposure

Benefits

Comp & perks

Competitive compensation with salary and equity
Comprehensive health coverage for you and your dependents
Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
Relocation support for employees moving to join the team in one of our office locations
A high-trust, low-ego culture that values diversity of thought, ownership and initiative, with respect for personal boundaries and working styles