IND Lead Software Engineer - GCC093 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. 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. Required Skills &

Experience

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. 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).

About Us

| Our Culture | What It’s Like to Work Here Every day, a day to do right. Showing up for people isn’t just what we do. It’s who we are – and have been for more than 200 years. We’re devoted to finding innovative ways to serve our customers, communities and employees—continually asking ourselves what more we can do. Is our policy language as simple and inclusive as it can be? Can we better help businesses navigate our ever-changing world? What else can we do to destigmatize mental health in the workplace? Can we make our communities more equitable? That we can rise to the challenge of these questions is due in no small part to our company values that our employees have shaped and defined. And while how we contribute looks different for each of us, it’s these values that drive all of us to do more and to do better every day.

About Us

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