Staff Data Scientist, Strategic Insights & Analytics

10x Genomics•Pleasanton, CA

1d•$198,500 - $268,500

About The Position

The Staff Data Scientist is a high-impact individual contributor and lead on 10x Genomics’ central Strategic Insights & Analytics team. The role partners with senior business leaders to frame and answer strategic questions with rigorous analysis and recommendations, supporting company resource allocation, growth, and go-to-market strategies. The position embeds with teams to address high-leverage problems, then documents, automates, scales, and hands off improvements so they continue to deliver value. The role also builds durable analytical models, business context, and decision-support systems that are legible to both people and AI. Working closely with central Software Engineering, AI, and Data Platform teams, the Staff Data Scientist helps develop a unified analytical representation of the company, customers, and market using statistically rigorous, version-controlled models and internal products. The position covers strategic analysis, growth measurement, commercial planning, AI-enabled tools and workflows, predictive models, business review processes, customer and market segmentation, and data and semantic foundations. The role is a full-time position with a base salary range of USD 198,500–268,500 per year, and is eligible for equity grants, health and retirement benefits, and an annual bonus or sales incentive program.

Requirements

Bachelor’s or master’s degree in a quantitative discipline such as statistics, engineering, mathematics, computer science, or data science; alternatively, an MBA with a technical focus.
At least 8 years of experience in data science, analytics, corporate strategy, business operations, management consulting, or similar work using analytics and applied data science to solve complex business problems and drive strategic decisions.
Hands-on depth in cohort and retention analysis, regression, time series, causal inference, experiment design, and supervised and unsupervised modeling.
Experience deploying machine learning models into production to solve commercial or operational problems.
Experience shipping working systems using AI coding tools such as Claude Code, Cursor, or similar, and aptitude for learning new tools quickly.
Proficiency with SQL and Python for data manipulation and predictive modeling.
Experience with Git, GitHub, Snowflake, dbt or similar tools, and CI/CD tools.
Comfort building code-based, version-controlled reporting and dashboards that replace legacy tools such as Tableau or Power BI.
Strong bias for action and comfort working in a fast-paced environment with ambiguous requirements; ability to scope problems, verify work, and design for end users.
Outstanding communication and interpersonal skills, including the ability to present technical work to senior management, sales leadership, and non-technical audiences and influence cross-functional teams.

Responsibilities

Partner with leadership to frame and answer ambiguous, high-stakes questions about market opportunity, segment prioritization, pricing and monetization, market entry, and commercial investment; develop recommendations with explicit assumptions and trade-offs, and program-manage resulting cross-functional decisions and initiatives through to action.
Develop a quantitative understanding of growth, including customer acquisition and expansion by segment, activation, adoption, retention, cohort behavior, funnel conversion, and the incremental return and effectiveness of sales, marketing, and service investments.
Build demand, customer lifetime value, and sales, marketing, and service capacity models at territory, account, and instrument levels to support annual planning, quota and territory design, and in-year resource allocation.
Migrate planning models from spreadsheets to version-controlled models in GitHub.
Turn one-off analyses into durable systems, including AI-enabled tools and agentic workflows that surface risks and opportunities, predictive models for propensity, churn risk, and next-best action, and standardized business review processes tied to company goals.
Define and implement customer and market segments across systems, enriched with telemetry and other data.
Develop versioned metric definitions and business context, and improve the enterprise data warehouse and semantic layer using tools such as dbt, GitHub, and Snowflake.
Embed with functions to address their most important problems, scale effective solutions, and document, automate, and hand off improvements to replace manual work, enable agentic workflows, and support self-serve analytics and insights.