Director, Data Science: Data Science Tools

Remote from: USA
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Data Science & Analytics
Employment type: Full Time,
Job posted: 15 Jun 2026
Apply before: 15 Jul 2026
Experience level: Director
Views / Applies: 10 / 0

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AI Summary

This is a Director-level role focused on building internal tools, pipelines, and applications for data science workflows at USRM. The role involves designing Python packages, implementing MLOps best practices, and integrating AI agent capabilities. The ideal candidate has deep technical expertise in Python, workflow tools like Airflow and MLflow, and experience with AI frameworks. They will own the strategy for improving data science productivity across the organization. The position offers a hybrid schedule in select locations or remote work.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires a rare combination of deep technical skills in Python, MLOps, and AI agents, along with strategic thinking and leadership. The candidate must build tools used by many data scientists, which demands high quality and reliability.

Salary Analysis

AI Insight The salary for this Director-level role is not explicitly listed, but based on market data for similar positions in the US, the typical range is $150,000 to $280,000 annually. The median is approximately $210,000. Given the seniority and specialized skills required, the offered compensation is likely competitive within this range.

Key Skills

Python MLOps Airflow MLflow Pydantic FastAPI AI Agents LangChain RAG Statistical Modeling

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Director of Data Science: Data Science Tools role at USRM. With over 10 years of experience building scalable data science infrastructure and leading teams, I have a strong track record of developing internal tools and pipelines that significantly improve model development and deployment workflows.

I have extensive experience with Python, MLOps frameworks like Airflow and MLflow, and AI agent technologies such as LangChain and PydanticAI. In my previous role, I designed a package used by 50+ data scientists, reducing model deployment time by 40%. I am passionate about empowering peers with efficient tooling and driving best practices.

I thrive in collaborative environments and enjoy mentoring teams. I look forward to contributing to USRM's data science culture and strategy. Thank you for considering my application.

Sincerely,
[Your Name]

Possible Interview Questions

Describe a time you built an internal tool or pipeline that improved the efficiency of your team. What was the impact?

In my previous role, I developed a Python package that automated feature engineering and model evaluation. It reduced manual work by 30% and standardized best practices across the team. The package was adopted by 5 teams and cut model iteration time in half.

How would you evaluate and implement an AI agent capability in our tooling? Walk us through the process.

I would start by identifying a repetitive task that data scientists perform, such as hyperparameter tuning or data cleaning. Then I would evaluate frameworks like LangChain or PydanticAI to build an agent that can autonomously execute that task. After prototyping, I would test for accuracy and speed, and finally integrate it into our existing workflow, ensuring it has proper safeguards and monitoring.

Can you explain your experience with MLOps and how you promote best practices across an organization?

I have implemented MLOps pipelines using Airflow and MLflow, focusing on reproducibility, version control, and monitoring. I promote best practices by organizing internal talks, writing documentation, and setting up automated checks in CI/CD. I also pair with teams to help them adopt these practices in a way that fits their workflow.

How do you handle competing priorities when building tools for multiple data science teams?

I prioritize based on impact and urgency. I gather requirements from stakeholders, then create a roadmap that balances quick wins with long-term investments. I communicate clearly about timelines and adjust as needed. Regular feedback loops ensure the tools address the most critical pain points.

Describe a complex technical problem you solved in a data science tooling context. What approach did you use?

We had a model deployment pipeline that was failing due to dependency conflicts. I led a refactoring effort to containerize the environment and standardize dependencies across teams using a custom Python package and a centralized image registry. The solution improved deployment success rate from 70% to 99% and reduced onboarding time.

Description

The Data Science Infrastructure organization within USRM is hiring a Senior Technical Professional, Data Scientist to join the Data Science Tools team. This role will focus on improving the end-to-end modeling workflow for USRM Data Science by building internal tools, pipelines, and applications that streamline model development, evaluation, deployment, and iteration. The ideal candidate is highly technical, proactive, and motivated by building systems that help other data scientists work more efficiently.

**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:

Design and build internal tools, pipelines, and applications that improve model development, evaluation, and deployment
Own strategy and roadmaps for improving data science workflows and tooling across USRM
Design, build, and maintain Python packages used across the organization
Evaluate and implement AI agent capabilities in tooling using approaches such as MCP, RAG, PydanticAI, LangChain, or related frameworks
Work with workflow and modeling tools such as Luigi, Airflow, Celery, MLflow, H2O, scikit-learn, Optuna, and LightGBM, as well as Python development tools such as Pydantic, FastAPI, uv, ruff, and pytest
Promote MLOps and AI agent best practices in collaboration with groups such as Enterprise Data & Data Science
Stay current on developments in open-source data science frameworks, MLOps, and agentic coding practices
Help shape the direction of the Tools team and contribute to a culture of ownership, collaboration, and continuous improvement

The ideal candidate will have:

Professional experience building and maintaining Python-based data science or Machine Learning tooling used by multiple end users or teams
Worked with any of the following in a professional setting: Git, Bash/shell scripting, uv, pre-commit, ruff, pytest, or Pydantic
Built, deployed, or maintained workflows or pipelines using any of the following: Airflow, Luigi, Celery, Databricks, or MLflow
Implemented or supported AI/LLM-based tooling using frameworks such as PydanticAI, LangChain, MCP, or RAG
Developed, reviewed, or maintained internal Python packages, APIs, or data science applications using tools such as FastAPI, Streamlit, Dash, NiceGUI, or Plotly
Applied agentic AI techniques in day-to-day development and incorporate AI capabilities directly into tools and applications where they create meaningful value for data scientists

Qualifications

Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
Ability to establish and build relationships within and outside the organization.
Ability to give effective training and presentations to management and other groups.
Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
Broad knowledge of business drivers and market context.
Has a value driven perspective with regard to understanding of work context and impact.
Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience.

About Us

Pay Philosophy: The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.
At Liberty Mutual, our goal is to create a workplace where everyone feels valued, supported, and can thrive. We build an environment that welcomes a wide range of perspectives and experiences, with inclusion embedded in every aspect of our culture and reflected in everyday interactions. This comes to life through comprehensive benefits, workplace flexibility, professional development opportunities, and a host of opportunities provided through our Employee Resource Groups. Each employee plays a role in creating our inclusive culture, which supports every individual to do their best work. Together, we cultivate a community where everyone can make a meaningful impact for our business, our customers, and the communities we serve.
We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. To learn more about our benefit offerings please visit: https://LMI.co/Benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran’s status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.
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