Below are representative questions aligned to how we assess Data Scientists at The Hartford. Use them to prepare crisp, business-grounded answers with supporting examples and clear trade-off reasoning.

Technical and Domain Knowledge

Expect direct probes into your mastery of insurance modeling and ML fundamentals.

• How would you design a frequency/severity framework for Personal Auto losses? Why GLM for frequency and what distribution for severity?
• Walk through how you’d prevent leakage when engineering features for a claims triage model.
• Compare XGBoost vs. GLM for a pricing-related model. When is each appropriate at The Hartford?
• How do you ensure calibration and interpretability in a high-stakes model?
• Describe handling class imbalance in fraud detection and measuring business impact.

SQL and Data Engineering for Analytics

Demonstrate fluency with joins, windows, and reproducibility.

• Write a SQL query to compute rolling 12-month loss frequency per policy with proper exposure.
• How would you reconcile policy, billing, and claims tables to create a training set without leakage?
• Show how you’d detect and address data drift upstream of a model.
• What is your approach to surrogate keys and maintaining auditability?
• Optimize a slow query joining large claims and policy datasets.

Modeling, Experimentation, and Causality

Show you can design credible tests and reason about cause and effect.

• Design an A/B test to evaluate a new retention offer with seasonal effects.
• Explain propensity score matching for a marketing intervention where RCT isn’t possible.
• How do you choose business metrics for model success (beyond AUC)?
• Describe building confidence intervals around expected lift.
• Discuss heterogeneous treatment effects and when they matter in retention.

MLOps, Monitoring, and Governance

We expect readiness for production and audit.

• Outline your model monitoring dashboard for claims triage (PSI, performance, routing SLA).
• What documentation would you include for model governance and why?
• How do you implement a champion/challenger framework responsibly?
• Describe your retraining cadence and drift thresholds.
• How do you approach fairness testing and explainability for regulatory audiences?

Communication and Stakeholder Leadership

Translate analytics into action and adoption.

• Give an example of influencing an underwriting or claims decision with your analysis.
• How do you explain rate impact from a model to non-technical leaders?
• Describe a time you managed trade-offs between accuracy and explainability.
• How do you handle a stakeholder who disagrees with your model recommendation?
• Walk through an executive summary you would present for a go/no-go decision.

Deep Dive into Evaluation Areas

Applied Machine Learning for Insurance Use Cases

Insurance is a specialized ML domain. You’ll be assessed on how you tailor algorithms to exposure-driven data, long-tailed loss distributions, and strict explainability requirements. Expect to justify your choices in terms of rating fairness, regulatory transparency, and operational fit.

• Be ready to go over:
  • Frequency/Severity Modeling: GLM/Poisson/NegBin with exposure offsets; handling zero-inflation; severity via Gamma/Lognormal.
  • Tree Ensembles & Boosting: XGBoost/LightGBM/CatBoost for triage, fraud, and propensity; feature importance and SHAP for interpretability.
  • NLP on Claims/Notes: Text preprocessing, embeddings, classification for litigation propensity or routing; explainability on text features.
  • Advanced concepts (less common): Hierarchical/credibility models, survival analysis for lapse/claim duration, uplift modeling for retention, semi-structured telematics data.
• Example questions or scenarios:
  • "Build a claim severity model and explain how you’d set the link function, handle outliers, and justify variable selection to actuarial."
  • "How would you prioritize features for a fraud detection GBM and defend the model to compliance?"
  • "Given claim notes and structured data, propose an NLP+tabular ensemble and your plan for model monitoring and drift detection."

Statistical Inference, Experimentation, and Causality

You’ll be asked to separate correlation from causation and design tests that withstand real-world constraints. The focus is on decision quality—not just p-values.

• Be ready to go over:
  • A/B testing & Incrementality: Sample sizing, CUPED, pre/post analyses, power, guardrails.
  • Causal Inference: Propensity scores, DID, IVs; when RCTs are impractical.
  • Uncertainty & Confidence: Intervals for lift, calibration of risk predictions.
  • Advanced concepts (less common): HTE estimation, Bayesian methods for small-signal environments.
• Example questions or scenarios:
  • "Design an experiment to evaluate a new retention offer, accounting for seasonality and channel bias."
  • "A price change shows improved quote binds—prove it’s causal, not mix shift."

Data Wrangling, Analytics Engineering, and SQL

The Hartford expects you to be strong with SQL, data quality triage, and feature engineering at scale. You’ll often join data from policy, claims, billing, and telematics, reconciling time windows and exposure logic.

• Be ready to go over:
  • SQL & Joins: Window functions, cohorting, leakage prevention, performance considerations.
  • Feature Engineering: Leakage-safe aggregates, rare-category handling, class imbalance.
  • Data Quality: Missingness analysis, audit trails, reproducibility.
  • Advanced concepts (less common): Spark/Databricks pipelines, feature stores, dbt-style modularity.
• Example questions or scenarios:
  • "Write a SQL to compute 12-month rolling claim frequency per policy, avoiding leakage."
  • "Given messy claim timestamps, normalize event sequences and create features for a triage model."

MLOps, Model Risk, and Governance

This area differentiates strong insurance data scientists. You’ll discuss deployment pathways, documentation, monitoring, and regulatory expectations.

• Be ready to go over:
  • Deployment: Batch vs. real-time scoring, API endpoints, latency/reliability trade-offs.
  • Monitoring & Drift: PSI/CSI, performance decay, recalibration schedules, champion-challenger.
  • Governance: Purpose statements, validation, fairness testing, access controls, audit readiness.
  • Advanced concepts (less common): Model cards, lineage, automated compliance checks.
• Example questions or scenarios:
  • "Outline a monitoring plan for a claims triage model—what signals and thresholds do you track?"
  • "How do you prepare a rate-impact model for regulatory review and stakeholder sign-off?"

Communication, Business Acumen, and Stakeholder Management

You’ll be evaluated on how you translate model insights into operational decisions and gain adoption from non-technical partners.

• Be ready to go over:
  • Framing: Clarify problem, constraints, and value levers.
  • Storytelling: Distill technical content for executives and regulators.
  • Change Management: Pilot design, training, and user feedback loops.
  • Advanced concepts (less common): NPV/IRR framing for analytics investments, capacity-aware optimization.
• Example questions or scenarios:
  • "Explain your model’s business case to an underwriting VP in three slides."
  • "Share a time you changed a stakeholder’s mind using data—and how you handled pushback."