Senior Data Scientist – VC Backed Startups

• Partner with product, engineering, and business leaders to identify high-impact data science opportunities
• Design and analyze experiments for product changes, growth initiatives, and operational strategies
• Develop predictive, forecasting, recommendation, ranking, and optimization models
• Apply statistical methods and causal inference techniques to measure impact and inform decisions
• Build metrics, analytical frameworks, and dashboards to improve visibility into product and business performance
• Translate complex analyses into clear recommendations for technical and non-technical stakeholders
• Collaborate with engineers to productionize models and integrate data science into customer-facing products
• Identify patterns in user, customer, operational, and market data
• Establish best practices for experimentation, model evaluation, data quality, and analytical rigor
• Mentor other data scientists and raise the technical standard of the broader data organization
• Help shape the company’s data strategy, tooling, and long-term analytical roadmap

Requirements

• 5+ years of experience in data science, applied statistics, machine learning, decision science, or a related field
• Strong proficiency in Python, R, SQL, or similar analytical languages
• Experience with statistical modeling, experimentation, causal inference, forecasting, or predictive analytics
• Track record of using data to influence product strategy, customer outcomes, or business performance
• Ability to work with large, complex, and imperfect datasets
• Experience partnering with product managers, engineers, operators, and executive stakeholders
• Strong communication skills and ability to explain technical findings clearly
• Experience developing models or analytical systems used in production or operational decision-making
• Strong judgment around methodology, measurement, tradeoffs, and uncertainty
• Experience in venture-backed startups or rapidly scaling technology companies may be preferred
• Advanced degree in statistics, economics, computer science, mathematics, operations research, or a related field may be preferred, but is not always required
• Familiarity with technologies such as pandas, NumPy, SciPy, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Snowflake, BigQuery, Redshift, Databricks, Spark, Looker, Tableau, Mode, Hex, Amplitude, Jupyter, dbt, Airflow, Git, Docker, and cloud platforms

Core Competencies

Demonstrates expertise in data science, statistical modeling, and machine learning, with a strong ability to translate complex analyses into actionable insights for diverse stakeholders. Proficient in developing predictive models and analytical frameworks to drive product and business performance.