What is a Data Scientist?

As a Data Scientist at The Hartford, you transform complex insurance data into decisions that protect customers and power growth. Your work underpins how we price policies, detect fraud, optimize claims, and enhance customer experiences across Personal Lines, Small Commercial, Middle & Large Commercial, and Specialty businesses. You’ll convert raw data into reliable, explainable models that directly influence underwriting, claims triage, marketing efficiency, and product strategy.

Expect to contribute to initiatives like auto pricing GLMs and GBMs, claims severity and litigation propensity models, fraud and subrogation detection, customer retention uplift modeling, call center/NLP automation, and time-series reserving support. The work is technically rigorous and business-critical: models may require regulatory filings, detailed model governance documentation, and alignment with the economics of insurance (loss ratios, combined ratios, and capital efficiency).

This role is compelling because you sit at the intersection of advanced analytics and high-impact business decisions. You’ll partner with actuaries, underwriters, claims leaders, and product teams, deploying models via modern data platforms and ensuring models are not just accurate—but interpretable, reliable, fair, and auditable. You’ll see your work reach production and change outcomes for customers and the company.

Getting Ready for Your Interviews

Your preparation should emphasize insurance-relevant modeling, business translation, and model governance—alongside strong fundamentals in Python/R, SQL, experimentation, and cloud-based deployment. Approach each interview as a chance to show how you scope a business problem, build an explainable solution, and land it operationally with measurable value.

• Role-related Knowledge (Technical/Domain Skills) - Interviewers look for fluency in supervised learning (GLM, tree ensembles), causal inference/experimentation, NLP on unstructured text (claim notes), forecasting, and fundamentals like feature engineering, EDA, and model evaluation. Demonstrate mastery of insurance modeling patterns (frequency/severity, exposure, offsets) and explain when/why you choose one approach over another.
• Problem-Solving Ability (How You Approach Challenges) - Expect scenario-based cases that test how you frame ambiguous objectives, identify constraints (compliance, interpretability, deadlines), and trade off accuracy vs. explainability. Show structured thinking, crisp assumptions, and business-first metrics (lift to loss ratio improvement, claim cycle-time reduction).
• Leadership (How You Influence and Mobilize Others) - You’ll be assessed on collaboration with actuaries/underwriters, ability to secure buy-in, and influence cross-functional decision-making. Demonstrate how you co-created solutions, handled disagreement with data, and mentored others.
• Culture Fit (How You Work with Teams and Navigate Ambiguity) - The Hartford values integrity, accountability, customer focus, and continuous improvement. Show resilience, ownership, and a pragmatic mindset—especially around governance, fairness, and responsibility in AI use.

Interview Process Overview

The Hartford’s process emphasizes real-world application over academic perfection. You’ll experience a balanced sequence of technical deep-dives, business-focused case discussions, and behavioral conversations that mirror how our teams operate day-to-day. Interviews are rigorous but respectful of your time; expect a focused pace that values clarity, evidence, and collaboration.

You’ll notice a consistent emphasis on explainability and governance—a hallmark of modeling in a regulated industry. You may be asked to articulate model purpose statements, outline documentation artifacts, or discuss model monitoring plans. The process is designed to uncover how you make decisions, communicate trade-offs, and ensure your models stand up to scrutiny from product, actuarial, compliance, and operations stakeholders.

This visual timeline shows the typical flow from recruiter conversation through technical and case interviews, with potential take-home or live case work and a collaborative panel. Use it to plan your preparation cadence and downtime between steps. Be proactive: clarify expectations for any case, confirm tooling preferences, and request business context early.

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."

This visualization highlights the most frequent topics in recent interviews and postings—expect concentration around GLM/GBM, SQL, NLP, governance, and business translation. Use it to prioritize your study plan and to tailor your project stories to the areas of greatest emphasis.

Key Responsibilities

You will own the lifecycle from problem framing to production impact. Day to day, you’ll scope opportunities with business partners, perform EDA, build and validate models, coordinate deployment with engineering, and communicate outcomes to senior stakeholders. You’ll document your work for governance and continuously monitor performance post-launch.

• Develop models for pricing, claims triage, fraud detection, retention, and experience enhancement—balancing accuracy with explainability.
• Partner cross-functionally with actuaries, product managers, underwriters, claims leaders, engineering, and compliance to align on goals and delivery.
• Build data pipelines and features using SQL/Spark; ensure reproducibility and auditability.
• Deploy and monitor models via enterprise platforms (e.g., cloud services, MLflow-style tracking); manage champion/challenger rotations.
• Communicate insights through clear narratives, visualizations, and executive-ready artifacts; support regulatory and internal reviews.
• Mentor junior team members; contribute to standards, templates, and best practices across the data science community.

Role Requirements & Qualifications

Successful candidates pair technical rigor with insurance-savvy decision-making. We expect depth in modeling and data, fluency with production-minded practices, and maturity in stakeholder engagement.

• Must-have technical skills
  • Programming: Strong Python (pandas, NumPy, scikit-learn); R is a plus for GLM/actuarial workflows.
  • SQL: Complex joins, windowing, cohorting, performance tuning.
  • ML Techniques: GLM/Poisson/NegBin, tree ensembles (XGBoost/LightGBM/CatBoost), calibration, model interpretation (SHAP/partial dependence).
  • Data Platforms: Experience with big data tools (Spark/Databricks) and major cloud platforms (e.g., AWS/Azure).
  • MLOps & Governance: Versioning (Git), experiment tracking, deployment patterns, monitoring, documentation for model risk/governance.
• Domain and analytics experience
  • Insurance knowledge: Frequency/severity modeling, exposure/offsets, loss ratio mechanics, underwriting/claims workflows, regulatory considerations.
  • Experimentation/Inference: A/B testing, causal inference basics for marketing/operations.
  • NLP and unstructured data: Text classification/extraction for claim notes or customer communications.
• Soft skills
  • Business storytelling, stakeholder alignment, and executive communication.
  • Problem structuring under ambiguity; prioritization and delivery focus.
  • Collaboration across actuarial, product, and operations; mentorship and peer review.
• Experience levels
  • Data Scientist: Typically 2–5 years relevant experience or advanced degree with strong internships/projects.
  • Senior Data Scientist: 5+ years with demonstrated production impact, governance leadership, and cross-functional influence.
• Nice-to-have
  • Experience with time-series/reserving support, uplift modeling, survival analysis.
  • Familiarity with Tableau/Power BI, MLflow, feature stores, and API-based deployments.
  • Prior work on regulatory-facing models or rate filings.

This view summarizes typical compensation bands for Data Scientist and Senior Data Scientist roles by market and experience. Use it to calibrate expectations and prepare informed questions about level, location (e.g., Hartford, CT, Charlotte, NC, Washington, DC), and total rewards.

Common Interview Questions

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.

Use this interactive module on Dataford to practice questions by category, difficulty, and format. Rehearse aloud, test your structure under time constraints, and iterate based on targeted feedback to close any gaps.

Frequently Asked Questions

Q: How difficult are the interviews, and how much time should I prepare?
Expect medium-to-high rigor with a strong real-world lens. Allocate 2–3 weeks to refresh ML/statistics, SQL, and to craft 3–4 business-impact case stories highlighting governance and deployment.

Q: What makes successful candidates stand out?
They connect modeling choices to insurance economics, articulate governance-readiness, and communicate with precision. They show a repeatable approach from framing to monitoring—not just a collection of algorithms.

Q: What is the typical timeline?
Processes can move quickly once interviews begin, often within 2–4 weeks depending on scheduling and level. Communicate availability proactively and confirm expectations for any take-home or live case.

Q: Is the role hybrid or on-site?
Roles may be hybrid in hubs like Hartford, CT, Charlotte, NC, and Washington, DC (for economist-leaning roles). Discuss specifics with your recruiter, including flexibility and collaboration expectations.

Q: Will I need to code live?
Plan for hands-on SQL and practical Python exercises or case discussions. The emphasis is on clarity, correctness, and reasoning—not trick puzzles.

Q: How much does model governance matter?
A lot. Be prepared to discuss documentation, validation, fairness, monitoring, and how you explain models to regulators and non-technical leaders.

Other General Tips

• Anchor on business impact: Translate metrics to loss ratio movement, cycle time reduction, or retention lift. Interviewers reward business fluency.
• Lead with explainability: Show SHAP, partial dependence, and GLM interpretability—then accuracy. This mirrors regulatory expectations.
• Preempt leakage and drift: State your controls up front; it signals production readiness and governance maturity.
• Practice SQL out loud: Narrate assumptions, keys, and windows. It demonstrates clarity and reduces mistakes under pressure.
• Use the ‘decision lens’: Ask how your case solution will be used operationally; optimize for that, not just AUC.
• Document as you go: Mention templates, model cards, and validation checklists. It shows you build auditability into your workflow.

Summary & Next Steps

This is a high-impact role at The Hartford where technical excellence meets business stewardship. You’ll build models that inform pricing, optimize claims, detect fraud, and improve retention—work that is both analytically demanding and operationally meaningful. Success comes from delivering models that are accurate, interpretable, compliant, and adopted by stakeholders.

Center your preparation on five pillars: insurance-tailored ML, experimentation/inference, SQL and data engineering, MLOps and governance, and executive-ready communication. Use the practice questions and refine 3–4 strong project narratives demonstrating end-to-end ownership—from framing to monitoring and measurable value.

You’re ready to bring clarity to complexity and drive outcomes that matter for customers and the business. Continue exploring insights and interactive practice on Dataford, align your stories to the evaluation areas above, and step into your interviews with confidence and purpose.