Clearco - Senior Data Scientist
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 • 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.) • At Clearco, we strive for an inclusive, accessible recruitment process. If you have specific accessibility needs, please let us know so we can support you. • Please note that we use AI-assisted tools to help manage applications, but humans remain the sole decision-makers in our hiring. Contact us for more information on our tools or to request an accommodation.
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.
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT