Assistant Director, Data Science, Modeling Sophistication

Liberty Mutual Insurance • Boston, MA

107d

About The Position

At Liberty Mutual, the Insights & Solutions group uses data, analytics, and technology to deliver innovative solutions that drive our US Retail Markets business forward. Within it, the Modeling Sophistication, Deep Learning Research team applies cutting-edge computer vision and deep learning to reimagine how we assess risk, design products, and serve customers. With a culture of rigor, reproducibility, and innovation, we turn complex images and high-dimensional data into actionable insights that power smarter decisions and drive real impact in insurance. As a Data Scientist, you will work with a multidisciplinary team of researchers and engineers to design, develop, and deploy computer vision and deep learning models. You will be responsible for translating research prototypes into production-ready solutions that deliver measurable business value. In addition to model development, you will contribute to methodological advancements, scalable data infrastructure, and cross-team scientific collaboration.

Requirements

• Demonstrated expertise in deep learning with an emphasis on computer vision.
• Strong foundation in machine learning, statistics, experimental design, and model evaluation metrics.
• Proficiency in Python and MLOps practices, with experience in version control (Git), code review, collaborative development workflows (e.g., GitHub/GitLab), and model versioning/experiment tracking (e.g., MLflow).
• Proficiency in deep learning frameworks such as PyTorch (preferred) or TensorFlow, with experience in model design, training, and deployment.
• Experience building and managing pipelines with workflow orchestration tools (e.g., Airflow, Luigi).
• Experience with Docker and CI/CD pipelines.
• Experience with container orchestration systems such as Kubernetes.
• Understanding of GPU acceleration, distributed training, and model optimization techniques (e.g., mixed precision, pruning, quantization).
• Experience with multimodal learning, including vision-language models and cross-modal representation learning.
• Track record of advancing research projects from ideation to implementation.

Nice To Haves

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Expert knowledge of predictive toolset; reflects as expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
• Ability to give effective training and presentations to peers, management and less senior business leaders.
• Ability to use results of analysis to persuade team or department management to a particular course of action.
• Has a value driven perspective with regard to understanding of work context and impact.

Responsibilities

• Design, train, and deploy computer vision and deep learning models, from research and experimentation through production implementation.
• Collaborate with business stakeholders to deliver data products such as feature pipelines, predictive models, dashboards, and datasets derived from image data.
• Develop and maintain scalable data pipelines and model workflows, applying MLOps best practices for reproducibility, deployment, and monitoring.
• Research and prototype new methodologies for training, evaluating, and improving deep learning models, particularly for computer vision.
• Integrate model outputs into business applications and partner with engineering teams to operationalize models in production environments.
• Contribute to the design, construction, and validation of large and complex datasets in collaboration with cross-functional science teams.
• Communicate findings through technical presentations, reports, and recommendations to both technical and non-technical stakeholders.
• Participate in cross-functional working groups and contribute to the broader data science community to promote best practices.

Benefits

• Comprehensive benefits and continuous learning opportunities.
• Strong relationships and a supportive work environment.
• Opportunities for professional and personal success.

What This Job Offers

• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees