Analyst I, Data Science: Infrastructure

Description

If you’re passionate about data science and want to see your work make a real impact, this role is a great opportunity to grow. As an Analyst I, Data Science, you’ll help bring machine learning models to life by supporting the tools, pipelines, and processes that move them from development into production. You’ll work closely with talented data scientists, ML engineers, IT partners, and business teams to make model deployment faster, smoother, and more scalable. It’s a chance to build your technical skills, learn about MLOps, and contribute to meaningful work that helps drive innovation across Liberty Mutual.

 

USRM Data Science Infrastructure is seeking an Analyst I, Data Science to support MLOps initiatives that accelerate model deployments. This role will collaborate with vertical leaders within the non-pricing deployments team in US Data Science (USDS) and work cross-functionally with data scientists, ML engineers, IT, and business stakeholders to deliver model deployment solutions.

 

You will help advance Liberty Mutual’s MLOps capabilities by supporting the transition of models from development to production while contributing to operational excellence and innovation. You will work with cross-functional teams to help build and maintain scalable, reliable deployment solutions aligned with Frontier’s business and technology transformation.

 

**Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel. **

 

Responsibilities:

• Support vertical leads with deployment needs, including building inference data pipelines, assisting with feature engineering, and contributing to model orchestration workflows.

• Collaborate closely within USDS, USDS Infrastructure, and across US Retail Markets technology to support model deployments and MLOps capabilities.

• Contribute to roadmaps that improve USDS model deployment capabilities and operational efficiency.

• Help ensure current and future deployments align with Frontier’s technology transformation.

• Use internal and external deployment tools such as Runway and Databricks to support model deployment for USDS.

• Collaborate with teams outside of USDS, including Enterprise Data and Data Science, the Data Office, Risk Analytics, and USRM IT, to improve model deployments and MLOps best practices.

• Stay current with developments in data science open-source frameworks, MLOps practices, and deployment tooling.

 

Preferred Qualifications

• Proficiency in Python and SQL

• Exposure to cloud platforms such as AWS/Azure.

• Familiarity with Docker, CI/CD, or workflow automation tools.

• Exposure to orchestration or scheduling tools such as Airflow or similar

• Experience working with APIs, data pipelines, or batch processing.

• Exposure to model monitoring, experiment tracking, or model registry tools.

• Familiarity with model lifecycle concepts such as versioning, testing, monitoring, and reproducibility

• Familiarity with software development practices, testing, code review, and documentation.

• Ability to document technical processes clearly and support reproducible workflows.

Qualifications

• Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
• Advance knowledge of predictive toolset; expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Has a value-driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through a Master’s degree (scientific field of study) and 0-1 years of relevant experience or a Bachelor’s degree (scientific field of study) and 3+ years of relevant experience.

 

Employees may apply for a new role after completing 12 months of employment in their current position.

 

Employees should review all role requirements and apply only for positions for which they are eligible. Hiring processes may vary by country, including differences in procedures, requirements, and timelines.  For country-specific details, please consult your local recruiting / HR team.