Lead, Strategic Insights & Analytics

10x Genomics•Pleasanton, CA

9h•$198,500 - $268,500

About The Position

The Lead on 10x Genomics’ Strategic Insights & Analytics team is a high-impact individual contributor in a flat, central organization, working directly with senior business leaders and embedding across teams to address high-leverage problems. The role combines strategic partnership with hands-on analytics and product-minded building. Its primary focus is improving decisions about company resource allocation, growth, and go-to-market strategy; its secondary focus is building cumulative business context and analytical models that can support decision-making through durable tools and infrastructure. The role partners closely with central Software Engineering, AI, and Data Platform teams to develop a unified analytical representation of the company, its customers, and its markets. This includes creating statistically rigorous, version-controlled models and deploying them as internal products. The Lead turns ambiguous, high-stakes business questions into evidence-based recommendations, then helps drive resulting decisions and initiatives through to action. The position also builds durable decision-support systems, predictive models, AI-enabled tools, and agentic workflows; strengthens data and semantic foundations; and replaces manual analysis and spreadsheet-based planning with scalable, documented, and automated processes. The Lead embeds with functions, scales effective improvements, and hands them off so they continue to deliver value.

Requirements

Bachelor’s or master’s degree in a quantitative discipline such as Statistics, Engineering, Mathematics, Computer Science, or Data Science, or an MBA with a technical focus.
At least 8 years of experience in data science, analytics, corporate strategy, business operations, management consulting, or similar roles 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 models.
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 quickly learning new tools.
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 one’s own work, and design for end users.
Outstanding communication and interpersonal skills, including presenting technical work to senior management, sales leadership, and non-technical audiences and influencing cross-functional teams.

Responsibilities

Partner with leadership on high-stakes questions involving market opportunity, segment prioritization, pricing and monetization, market entry, and commercial investment; frame decisions, develop evidence and recommendations, and communicate assumptions and trade-offs.
Program-manage cross-functional decisions and initiatives through to action.
Build a quantitative understanding of growth, including new and existing customers by segment, activation, adoption, retention, expansion, cohorts, funnel conversion, and the incremental return and effectiveness of sales, marketing, and service investment.
Develop demand, customer lifetime value, and sales, marketing, and service capacity models at territory, account, and instrument levels to inform 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.
Deploy predictive models, such as propensity, churn-risk, and next-best-action models, where they can influence frontline decisions.
Establish a unified business review cadence using standardized views 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 important problems, scale effective solutions, and document, automate, and hand off improvements.
Replace manual work with agentic workflows and enable self-serve analytics and insights.