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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.
Job Responsibility:
Rebuild our sales forecasting model from a rules-based spreadsheet system to a production ML pipeline
Redesign our territory performance framework to better account for market heterogeneity across academic vs. pharma accounts
Develop early-warning signal models that surface churn risk, stalled accounts, and expansion opportunities before they become visible to the field
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
Define model evaluation frameworks and own ongoing performance monitoring
Design specific performance frameworks for distributor-led markets (APAC/EMEA)
Conduct deep-dive analyses on quota attainment, territory performance, and account penetration
Conduct deep customer analyses that map account health, buying patterns, and product adoption across segments
Act as the commercial 'voice of the data' to our Product and Software Engineering teams
Develop and maintain price elasticity models that define the 'scientific floor' for global discounting
Analyze historical discounting patterns across regions and account types (Academic vs. Pharma)
Build and own early-warning models that detect churn risk, stalled pipeline, and expansion readiness
Develop feature sets from disparate sources such as Salesforce activity, ERP order history, analytical tool usage patterns, and field notes
Establish a signal monitoring framework that automatically flags at-risk and high-opportunity accounts
Translate ambiguous business questions into structured analytical frameworks
Mentor junior analysts on the team
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 have:
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
What we offer:
equity grants
comprehensive health and retirement benefit programs