Senior Data Scientist
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Job Description
Senior Data Scientist at Clearco enhancing risk and revenue models through data science and machine learning. Collaborating with cross-functional teams to drive analytics and model production.
Responsibilities:
- Design and execute data science experiments such as causal analysis, A/B tests, and offline evaluations to validate product and underwriting decisions.
- Develop, evaluate, and iterate on predictive models (e.g., credit/risk scoring, revenue forecasting, policy performance).
- Own model performance and monitoring: define success metrics, investigate drift, and drive improvements to data quality and feature reliability.
- Partner with Product Engineering to productionize models and analytics, focusing on reliability, reproducibility, and maintainability.
- Turn messy real-world data into usable signals through exploratory analysis, feature engineering, and robust validation.
- Clearly communicate insights to both technical and non-technical stakeholders through documentation and presentations.
- Raise the bar for technical quality via improved analytical standards, code review practices, and documentation.
- Mentor and support other team members through pairing, feedback, and sharing best practices.
Requirements:
- 5+ years of professional experience in data science, applied machine learning, or a related quantitative role
- Strong foundations in statistics and experimentation (hypothesis testing, causal reasoning, bias/variance tradeoffs, evaluation design)
- Proven experience building and shipping predictive models (classification, regression, time series, etc.) and measuring real-world impact
- Proficiency in Python and SQL, with comfort working with production data workflows
- Comfortable working with stakeholders to define problems, align on success metrics, and deliver outcomes end-to-end
- Strong written communication skills and a pragmatic approach to fast-moving environments
- Nice to Have: Experience with credit risk, underwriting, fraud/risk signals, or financial forecasting, familiarity with modern data tooling and warehouses (e.g., BigQuery, Snowflake) and transformation frameworks (e.g., dbt), experience with MLOps patterns (model deployment, monitoring, feature stores, orchestration) and cloud environments, experience working with messy third-party data sources (banking data, eCommerce platforms, marketing signals, etc.)
Benefits:
- None specified
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