Snowflake18 days ago
Staff Data Scientist, Finance
Salary not specified
MARKET
15,900 ₽median for this role
Data Scientist · 115 jobs with disclosed pay
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Полная занятостьУдалёнка
Responsibilities
- 01Own and scale a standardized driver-based revenue modeling framework across Snowflake's product categories
- 02Define clear driver trees, attribution rules, measurement standards, assumptions, and taxonomies
- 03Develop statistical, econometric, and machine learning methods to identify leading indicators
- 04Forecast key drivers and revenue across short- and long-range horizons
- 05Build self-service scenario, decomposition, and what-if tools
- 06Establish high standards for point-in-time evaluation, backtesting, stability testing, forecast reconciliation, confidence intervals, attribution, and documented model or assumption changes
- 07Productionize and operate frequently refreshed pipelines and applications with strong data-quality gates, monitoring, anomaly detection, versioning, reproducible backfills, and safe lifecycle management
- 08Partner closely with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams
- 09Communicate clearly with senior leaders about the drivers behind forecast movements
- 10Raise the bar for technical rigor and reusable standards through mentorship and technical leadership
- 11Set cross-category direction and influence the broader modeling roadmap
Requirements
- 01Advanced degree in Statistics, Mathematics, Operations Research, Economics, Engineering, Computer Science, or a related quantitative field, or equivalent practical experience
- 025+ years of experience building and operating production-grade statistical, forecasting, econometric, or machine learning systems with meaningful business impact
- 03Strong hands-on experience with business-critical forecasting, driver-based or unit-economics modeling, financial planning, demand or capacity planning, or other systems that connect operational inputs to business outcomes
- 04Deep modeling skills, including strong judgment around time-series forecasting, causal inference, panel or cohort methods, segmentation, hierarchical or probabilistic models, and when a simpler approach is more reliable than a more sophisticated one
- 05Ability to work with imperfect or limited telemetry, define defensible assumptions, identify and close data gaps, and distinguish true business movement from instrumentation changes, one-time events, timing shifts, and model artifacts
- 06Strong proficiency in Python and SQL