Senior Software Engineer/Data Scientist
At a glance
Senior Data Science role at The Hartford. India GCC-Puppalaguda Village · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Build GLM and GBM models, check their performance and stability, and explain findings to technical and business audiences
- Validate incoming third-party data, test it over time, and work with vendors to resolve quality issues
- Work with business partners to shape analytical questions and turn results into practical recommendations
- Automate analytical processes, support work across coverage lines, and track model and business measures
- Help with A/B tests, drift monitoring, and documentation for privacy, compliance, and responsible modeling
What you bring
- Four to six years of experience, plus a degree in computer science, mathematics, data science or a related discipline
- You use Python for statistical modeling and machine learning, and bring strong SQL skills
- You can assess model stability, spot overfitting, and explain results and tradeoffs clearly
- You have worked with multiple data sources, checked data quality, and addressed issues with external providers
- You use Git and Unix-based environments, with experience across model framing, testing, evaluation, and validation
Who this fits
This role suits a senior data scientist who wants to deepen modeling expertise while taking on more responsibility. You’ll work with Product, Underwriting, Risk, business stakeholders, and external data vendors. The listed location is India.
From the employer
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Position Overview
The Data Scientist is responsible for building and deploying analytical and machine learning solutions that address complex business and regulatory needs. The role works closely partners across Product, Underwriting, and Risk to deliver scalable, secure, and production-ready solutions.
Successful candidates combine solid statistical modeling and ML fundamentals, strong Python skills, and a growing ability to communicate analytical outcomes to business partners. This role is well-suited for someone who brings intellectual curiosity, a bias toward action, and a collaborative mindset, and who is looking to deepen their modeling expertise while taking on increasing responsibility over time.
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Key Responsibilities
• Modeling & Evaluation: Build and evaluate models using GLMs, GBMs, and related approaches. Assess model performance and stability, diagnose overfitting, and document findings clearly for technical and non-technical audiences.
• Third-Party Data & Vendor Support: Assist in managing third-party data relationships, including data intake, validation, and iterative testing. Engage with external vendors to resolve discrepancies and ensure data quality.
• Business Partnership & Communication: Collaborate with business stakeholders to understand analytical objectives and contribute to translating results into clear recommendations. Develop comfort presenting findings and explaining tradeoffs to partners with varying levels of technical fluency.
• Analytical Execution: Contribute to process improvement and automation efforts to reduce manual effort and increase analytical throughput. Support work across multiple lines of coverage with attention to rigor and consistency.
• Monitoring & Governance: Help define and track metrics for classification, forecasting, and business KPIs. Support A/B testing, monitor for drift, and contribute to compliance, privacy, and responsible modeling standards.
• Continuous Learning: Stay current on developments in ML, statistical modeling, and best practices. Build familiarity with the broader analytical toolkit and contribute to reusable templates and documentation.
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Required Skills & Experience
Experience Range - 4 to 6 Years
Modeling & Statistics
• Working knowledge of GLMs and GBMs in Python, with an understanding of when each approach is appropriate (e.g., GBMs for exploration and interaction detection; GLMs for interpretability and implementation readiness).
• Ability to assess model stability, identify overfitting, and communicate results and tradeoffs clearly.
• Familiarity with ML lifecycle best practices including documentation, version control (GitHub), and experiment tracking (e.g., MLflow).
Data & Vendor Support
• Experience with data validation, quality checks, and working across multiple data sources simultaneously.
• Comfortable engaging with external vendors or data providers to ask clarifying questions and resolve data issues.
Business Communication
• Ability to present analytical findings clearly to both technical and non-technical audiences.
• Developing skill in translating model results into actionable recommendations, including communicating uncertainty or limitations honestly.
Technical Foundations
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Data Science, or a closely related discipline.
• Experience in statistical modeling and machine learning using Python (pandas, NumPy, scikit-learn) with strong SQL skills.
• Across the modeling lifecycle: problem framing, experiment design, evaluation, and validation.
• Experience using Git and Unix-based development environments with reproducible analytical workflows.
• Familiarity with model monitoring concepts including drift detection and performance tracking.
• Some exposure to cloud-based platforms (Vertex AI, SageMaker, or Azure ML) is a plus.
• Familiarity with enterprise governance expectations including compliance, privacy, and model documentation standards.
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Nice to Have
• Experience in regulated modeling environments, including documentation and approval workflows.
• Familiarity with insurance pricing, segmentation, or rating variables.
• Familiarity with bias/fairness testing and model risk documentation.
• Exposure to generative AI or LLM concepts (RAG, prompt engineering, agentic workflows).
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