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Lead Data Scientist
Описание вакансии
Текст:
TL;DR
Lead Data Scientist (Financial Services, Python/SQL): Building the analytical backbone for lending and protection-products operations with an accent on data governance, credit and reserves analytics, and pricing experimentation. Focus on productionizing predictive models, designing controlled experiments, monitoring data and model quality, and translating analytical results into pricing, underwriting, and margin decisions.
Location: Remote, California, USA
Company
is building safe, reliable, customizable, and affordable electric vehicles in the USA.
What you will do
- Partner with Data Engineering to evolve the data lake and build a scalable, curated warehouse layer for loan, payment, claims, and pricing data.
- Maintain data dictionaries, field-level lineage, business definitions, ownership, and regulatory model documentation.
- Build automated data-quality monitoring for completeness, freshness, distributional drift, and referential integrity, including alerting and remediation workflows.
- Govern the Financial Services Tableau environment, including data sources, extract performance, permissions, workbook certification, and reporting retirement.
- Audit credit models, loan performance, reserves, and claims data, translating findings into pricing, underwriting, and reserve recommendations.
- Design and analyze A/B tests, forecasting, segmentation, propensity, and lifetime-value models while setting technical standards and influencing senior stakeholders.
Requirements
- 7+ years of applied data science or quantitative analytics experience, including at least 2 years at a senior or lead level.
- Bachelor's degree in statistics, mathematics, economics, computer science, engineering, or a related quantitative field.
- Expert SQL and Python, including pandas, scikit-learn, and statsmodels.
- Experience with distributed compute environments such as Spark, Snowflake, Databricks, or Redshift.
- Experience building, validating, and monitoring predictive models on financial or transactional data.
- Hands-on experience with controlled experiments, power analysis, guardrail metrics, inconclusive results, modern BI tools, and executive communication.
Nice to have
- Experience in consumer lending, auto finance, insurance, or protection and warranty products.
- Causal inference methods beyond A/B testing, including difference-in-differences, instrumental variables, synthetic control, or uplift modeling.
- Master's or PhD in a quantitative discipline.
Culture & Benefits
- Culture centered on safety, customer focus, innovation, mutual respect, and continuous improvement.
- Start-up spirit emphasizing ingenuity, resourcefulness, and frugal execution.
- Collaborative environment built around shared principles and respect for different backgrounds and work styles.
- Equal Employment Opportunity and reasonable accommodation for qualified individuals with disabilities.