Assistant Director, Data Science, Modeling Sophistication

Liberty Mutual-posted 15 days ago

Full-time • Manager

Seattle, WA

5,001-10,000 employees

Insurance Carriers and Related Activities

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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. This role may have in-office requirements based on candidate location Level of position offered will be based on skills and experience at manager discretion

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.

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.
Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 2 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and a minimum of 5+ years of relevant experience.

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.

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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