Associate Data Scientist, Personal Lines
At a glance
Junior Data Science role at The Hartford. Charlotte · full-time · $81,040–$121,560 base.
dataroles summary, based on the employer's posting.
What you'll do
- Create and assess models for personal auto pricing, loss costs and customer demand, then assist with putting them into production
- Test additional data and modeling methods, and shape useful inputs that strengthen the models
- Look after model repositories and scripts, streamline team processes, and help modernize cloud MLOps deployments
- Present findings to business partners, recommend actions, and answer questions from state insurance departments
- Keep analysis repeatable, review code, maintain Git standards, and share machine learning practices with colleagues
What you bring
- A master’s in statistics, data science or another listed analytical discipline
- You understand generalized linear models and methods such as ridge regression, lasso and GAMs
- You have practical Python experience and write clear code in a reproducible setup
- You have used GitHub to manage versions, documentation, collaboration and technical projects
- You can work in the US without employer sponsorship
Who this fits
This junior position suits someone who wants to build and support models throughout their lifecycle, working with product and pricing partners. The schedule requires office attendance Tuesday through Thursday in Columbus, Chicago, Hartford or Charlotte. The role cannot accommodate the STEM OPT I-983 Training Plan sign-off.
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.
The Hartford seeks an energetic and passionate Associate Data Scientist to build high-performing, empathetic analytics teams and deliver smarter product pricing decisions. This role will focus on the Auto line within the Personal Insurance Loss and Demand Modeling team. We support our product and pricing partners through the delivery of rigorously developed and tested solutions including models and implementation support.
In this role, you will contribute across the full model lifecycle, from data exploration and feature engineering through model development, validation, implementation, and monitoring. Generalized linear models are a core component of our work, alongside machine learning techniques such as gradient boosted trees and modern MLOps practices. You will also help improve the tools, workflows, and standards that enable the team to support a broad and evolving portfolio of models.
Responsibilities:
- Develop and evaluate predictive modeling solutions that support the personal auto class plan, unconstrained loss models, and customer demand models.
- Evaluate new data sources and modeling approaches to boost product performance. Engineer features to uncover additional signal for our models.
- Support model implementations with state Departments of Insurance (DOI), serving as a subject matter expert on the models you build and responding to DOI queries.
- Maintain a portfolio of repositories and scripts and suggest improvements to workflows. Assist in the modernization of model deployments using our cloud-based MLOps tools.
- Collaborate with team members to plan and deliver projects in an Agile framework.
- Develop and maintain analytic packages to facilitate data collection, analysis, and modeling.
- Stay up to date on machine learning techniques, apply them to your work, and share them with your team and the broader data science community.
- Ensure analytic reproducibility, conduct code reviews, and maintain git hygiene.
- Share results with partners and make recommendations based on outcomes.
This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).
Experiences & Skills:
- Master's degree in Statistics, Data Science, Mathematics, Actuarial Science, Economics, Engineering, Physics, or similar analytical field.
- Experience in the insurance industry, particularly in loss cost modeling or personal insurance products, is a plus but not required. Candidates with relevant modeling experience from other industries are encouraged to apply.
- Comfort with generalized linear models and related techniques (e.g., ridge and lasso regression, GAMs) and the math behind them.
- Hands-on experience with Python and a track record of delivering clean, literate code in a reproducible environment.
- Experience using GitHub for version control, documentation, code collaboration, and technical project management.
- Able to independently research solutions, identify when you are stuck, and seek help when needed.
- Strong interest in identifying and eliminating workflow inefficiencies. Passionate about tracking down the root cause of problems and identifying tradeoffs.
- Demonstrated experience with listening to collaborators and brainstorming outcomes that address their business problems.
- Experience building modeling solutions in cloud-native environments, such as SageMaker, a plus.
- Committed to democratizing knowledge, evangelizing good practices, and supporting peers.
Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$81,040 - $121,560Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
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