Lead Data Scientist
Slate AutoAbout the role
About Slate
At Slate, we’re building safe, reliable vehicles that people can afford, personalize and love—and doing it here in the USA as part of our commitment to reindustrialization. The spirit of DIY and customization runs throughout every element of a Slate, because people should have control over how their trucks look, feel, and represent them.
About The Role
Slate is hiring a Lead Data Scientist to own the analytical backbone of our lending and protection-products business. This is a senior individual-contributor role: you will set technical direction across our data platform, credit and reserves analytics, and pricing experimentation, and you will raise the bar for how the organization builds, validates, and monitors models. You will work directly with Accounting, FP&A/Pricing, Engineering, and external partners.
You will take on multi-quarter ownership of an ambiguous problem space, accountability for decisions that carry direct P&L and regulatory consequence, and influence over technical roadmaps within Financial Services.
What You'll Do
Data Platform & Governance
Collaborate with Data Engineering on the design and evolution of the data lake so that loan, payment, claims, and pricing data land reliably and are queryable at scale.
Define and maintain the curated warehouse layer: conformed dimensions, incremental fact builds, and certified marts from which Financial Services generates reports and dashboards.
Maintain an authoritative data dictionary with field-level lineage, business definitions, and ownership, and enforce it as the single source of truth in model documentation and regulatory submissions.
Build automated data quality checks for completeness, freshness, distributional drift, and referential integrity with alerting and documented remediation paths, and report quality SLAs to stakeholders.
Manage the Financial Services Tableau instance end to end: data source governance, extract performance, permissions, workbook certification, and retirement of stale or duplicative reporting.
Credit, Risk & Pricing Analytics
Audit credit models and loan payment performance by risk score, including vintage and cohort analysis, score-band migration, and early-warning indicators, and translate findings into pricing and underwriting recommendations.
Audit reserves and claims data for protection products, reconciling loss development against actuals and partnering with Finance and Accounting on reserve adequacy.
Propose, deploy, and analyze A/B tests to estimate price elasticity curves, and convert them into pricing and margin recommendations with explicit confidence intervals and revenue-at-risk framing.
Build and productionize forecasting and segmentation models (e.g. propensity/attach rate, lifetime value) and define the metrics the business uses to steer them.
Technical Leadership
Set the technical bar for the team: review analytical designs and code, define standards for reproducibility and version control, and drive adoption of shared tooling.
Communicate directly with senior stakeholders and control partners and defend methodology under scrutiny.
Identify and scope work no one has asked for yet: the analysis, dataset, or monitoring capability the business needs but has not articulated.
Minimum Qualifications
7+ years of applied data science or quantitative analytics experience, including at least 2 years operating at a senior or lead level with ownership of a problem space.
Bachelor's degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar).
Expert SQL and Python (pandas, scikit-learn, statsmodels); comfort working in a distributed compute environment such as Spark, Snowflake, Databricks, or Redshift.
Demonstrated experience building and validating predictive models on financial or transactional data, including how the model was monitored after deployment.
Hands-on experience designing and analyzing controlled experiments, including power analysis, guardrail metrics, and interpretation of inconclusive results.
Fluency in modern BI tooling (Tableau strongly preferred) and in the data modeling that makes it trustworthy.
A track record of written and verbal communication with non-technical executives.
Preferred Qualifications
Direct experience in consumer lending, auto finance, insurance, or protection/warranty products.
Causal inference beyond A/B testing: difference-in-differences, instrumental variables, synthetic control, or uplift modeling.
Master's or PhD in a quantitative discipline.
WHY JOIN TEAM SLATE?
At Slate, we’re fueled by grit, determination, and attention to detail. The start-up spirit of ingenuity and resourcefulness move our business forward. Team Slate fosters a culture of excellence, innovation, and mutual respect, and is motivated by shared principles.
Safety First
Delight Customers
One Team
Relentless Improvement
Fast, Frugal, and Scrappy
Respectful Collaboration
Positive Legacy
WE WANT TO WORK WITH PEOPLE THAT REFLECT THE COMMUNITIES IN WHICH WE OPERATE.
Slate is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, marital status, parental status, cultural background, organizational level, work styles, tenure and life experiences. Or for any other reason.
Slate is committed to providing reasonable accommodation for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at
slate-talent_acquisition@slate.auto.
Responsibilities
- Collaborate with Data Engineering on the design and evolution of the data lake.
- Define and maintain the curated warehouse layer for Financial Services.
- Build automated data quality checks and report quality SLAs.
- Audit credit models and loan payment performance.
- Propose, deploy, and analyze A/B tests for pricing recommendations.
- Set the technical bar for the team and drive adoption of shared tooling.
Qualifications
- 7+ years of applied data science or quantitative analytics experience.
- Bachelor's degree in a quantitative field.
- Expert SQL and Python skills.
- Experience building and validating predictive models.
- Hands-on experience designing and analyzing controlled experiments.
- Fluency in modern BI tooling, preferably Tableau.
Skills mentioned
About Slate Auto
At Slate, we’ve got one job: building a vehicle you’ll fall in love with. We believe that car buyers—not car companies—should call the shots. We like looking at a road, not a screen. We don’t like paying for stuff we don’t need. We like picking out the stuff we do need. We think dings are badges, not blemishes. But we know not everyone likes what we like. So, we built you a Slate: you make it yours.