Staff Data Scientist, Commercial Analytics

10x Genomics•Pleasanton, CA

12h

About The Position

10x Genomics sells high-complexity capital equipment and consumables into academic research institutions, biotech companies, and large pharma. Our commercial ecosystem involves long sales cycles, regional field teams, and a global distribution network which means forecasting and territory analytics here are genuinely hard and genuinely consequential. We’re hiring a Senior Data Scientist to own the commercial analytics function within our Business Insights & Analytics team. You’ll report to the Head of Business Insights & Analytics. This is a new headcount role created to deepen our ML capability as we continue to grow and scale globally. In year one, your primary mandate is threefold: (1) rebuild our sales forecasting model from a rules-based spreadsheet system to a production ML pipeline, (2) redesign our territory performance framework to better account for market heterogeneity across academic vs. pharma accounts, and (3) develop early-warning signal models that surface churn risk, stalled accounts, and expansion opportunities before they become visible to the field, turning reactive account management into proactive intervention.

Requirements

Master’s degree in a quantitative discipline such as Statistics, Engineering, Mathematics, Computer Science, Data Science, or an MBA with a technical focus.
7+ years of experience using analytics to solve complex business problems, including coding (Python or R), querying databases (SQL), and statistical analysis.
Production ML Experience: Proven track record of deploying machine learning models into production environments to solve commercial or operational challenges.
Technical Mastery: Expert-level proficiency in writing complex, efficient SQL and using Python/R for data manipulation and predictive modeling.
Commercial Acumen: Profound experience with sales processes and tools, specifically Salesforce CRM, sales quota/territory assignment, and the B2B enterprise SaaS demand generation funnel.
Data Visualization: Advanced knowledge of data visualization tools like Tableau or Power BI to synthesize data into actionable executive dashboards.
Stakeholder Management: Exceptional communication and interpersonal skills, with the ability to lead through ambiguity and influence cross-functional teams.

Nice To Haves

9+ years of experience in data science or commercial analytics within a high-growth environment.
MLOps Expertise: Experience with Machine Learning Operations (MLOps) tools and practices to maintain model health and scalability.
Industry Context: Experience working within a healthcare, life sciences multinational, or a leading tech-driven organization.
Strategic Depth: Proven ability to conduct technology assessments and ROI analysis for complex organizational system solutions.

Responsibilities

Own end-to-end development of ML-based sales forecasting models from feature engineering in Snowflake to deployment and monitoring in our AWS environment.
Build pipeline health and churn risk models that give our VP of Sales a forward-looking view beyond what’s visible in Salesforce today.
Define model evaluation frameworks and own ongoing performance monitoring; alert stakeholders when model drift requires recalibration.
Design specific performance frameworks for distributor-led markets (APAC/EMEA), creating proxy metrics to measure success where we do not own the "last mile" of customer data.
Conduct deep-dive analyses on quota attainment, territory performance, and account penetration, segmenting by geography, account type, product line, and sales rep tenure.
Conduct deep customer analyses that map account health, buying patterns, and product adoption across segments translating behavioral signals from Salesforce and analytical tool usage into a clear view of whitespace, risk, and untapped expansion potential.
Act as the commercial "voice of the data" to our Product and Software Engineering teams. You won't just analyze what we have; you will strategize what telemetry we need to collect from our instruments and software to better predict customer expansion and system utilization.
Develop and maintain price elasticity models that define the "scientific floor" for global discounting. You will translate these models into actionable pricing guardrails that are integrated directly into our CPQ (Configure, Price, Quote) workflows or regional sales guidelines.
Analyze historical discounting patterns across regions and account types (Academic vs. Pharma) to identify where we are leaving money on the table. You will ensure we are maximizing gross margin while maintaining a competitive win rate.
Build and own early-warning models that detect churn risk, stalled pipeline, and expansion readiness at the account level giving field teams actionable signals weeks before issues surface in lagging indicators.
Develop feature sets from disparate sources such as Salesforce activity, ERP order history, analytical tool usage patterns, and field notes to capture behavioral signals that precede customer disengagement or expansion.
Establish a signal monitoring framework that automatically flags at-risk and high-opportunity accounts, integrating outputs directly int Salesforce workflows so reps act on insights without leaving their existing tools.
Translate ambiguous business questions into structured analytical frameworks; communicate findings in plain language without hiding behind technical complexity.
Mentor junior analysts on the team; you won’t manage people, but you will be expected to raise the technical bar.