Risk Analytics Specialist

Job Title: Risk Analytics Specialist

Location: Onsite – Boston HQ

Employee Type: Full Time, Exempt – 50 hours/week

Compensation: $90,000 - $104,000* + Bonus + Full Benefits (day one)

Reports To: VP Risk

Join a dynamic startup pioneering a new category in the $90 billion automotive industry. Flexcar is the first and only zero‑down, month‑to‑month car lease, currently active in four markets and expanding rapidly. We're redefining what car ownership means.

At Flexcar, we don't sell cars. We sell freedom.

• Freedom from car loans
• Freedom from used car salesmen, insurance agents, and car mechanics
• Freedom to cancel anytime
• Freedom to drive any car, anytime

In automotive financing, Risk management determines profitability, growth, and regulatory sustainability. Traditional risk teams become blockers;
Flexcar reimagines Risk as a strategic growth partner through unified, data‑driven decision‑making.

This unique hybrid role combines three analytical perspectives in one position:

• Product Operations – Metrics ownership, feature evaluation, business case development
• Data Science – Predictive modeling, statistical rigor, hypothesis testing
• Risk Management – Underwriting principles, loss pattern analysis, policy implications

You’ll quantify growth‑risk trade‑offs, measure feature impact before scaling, bridge operational execution with statistical depth, and transform risk policies into data‑driven strategy.

Own daily, weekly, monthly, and quarterly tracking across 8 Risk functions: approval rates, fraud detection, delinquency patterns, compliance metrics, and feature impact. Become the "source of truth" for Risk performance.

Build predictive models for credit risk scoring, fraud propensity, and claims forecasting. Evaluate features through structured business cases: hypothesis → analysis → recommendation → deployment → monitoring. Example: "Lowering credit threshold to 620 increases approvals 3%, defaults 1.2%, net positive per customer."

Navigate competing stakeholder interests (Risk vs. Growth) through data credibility. Recommend policies backed by evidence. Success metric: >50% of recommendations adopted by leadership.

Conduct quarterly variance analysis connecting Risk metrics to financial impact. By Month 12, articulate how policy changes affect profitability and contribute to financial strategy discussions.

Design valid experiments (A/B tests, cohort analysis, scenario testing). Maintain documentation, dashboards, and processes. Improve data infrastructure and analytical tooling.

• Bachelor's degree in Statistics, Economics, Data Science, Computer Science, Mathematics, Finance, or Engineering
• 2‑4 years in data analytics, product operations, data science, risk modeling, or related analytical role
• SQL:
  Intermediate‑Advanced (joins, subqueries, window functions; complex query in 1‑2 hours)
• BI Tools:
  Tableau, Looker, Sigma, Lovable, or Power BI (design dashboards, create compelling visualizations)
• Statistics:
  Understand statistical significance, confidence intervals, A/B test design, hypothesis testing, Model Performance Management
• Python or R:
  Write analytical scripts independently, understand library documentation, debug code
• Predictive Modeling:
  Linear/logistic regression, decision trees, random forests, time series forecasting

• Communication:
  Explain complex technical findings to non‑technical stakeholders in accessible language
• Cross‑Functional
  
  Collaboration:
  
  Navigate competing interests, build credibility across Risk, Product, Finance
• Influencing Without Authority:
  Recommend policies through evidence and data credibility, not position
• Business Acumen:
  Connect analytics to P&L implications, understand Flexcar business model
• Domain experience (Fin Tech/Insur Tech/), cloud platforms (Snowflake/Sigma/Redash), Agile methodologies
• Pragmatism:
  Balance rigor with timeline pressure (80% accurate in 1 week beats perfect in 4 weeks)

• Supportive Leadership: Leadersh…