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.

Description
Solaria Labs is a cross-functional team that partners with teams from across Liberty Mutual to stay on the forefront of disruption, incubating new products, services and experiences to better serve our customers, partners and communities today and in the future.

• Collaborate with cross-functional teams across the enterprise to develop innovative predictive and generative AI solutions and solve Liberty's most challenging data science problems.
• Apply a broad range of sophisticated analytic techniques to manipulate large structured and unstructured datasets, generating insights that inform and drive business decisions.
• Research, design and develop cutting-edge solutions tailored to business needs.
• Identify and test hypotheses and build models for various business applications.
• Translate quantitative analyses and results into accessible visuals and high-level presentations for non-technical audiences to help stakeholders interpret the data.
• Enable the business to make clear trade-offs among choices, with a reasonable view into likely outcomes.
• Customize machine learning solutions to meet specific client requirements.
• Guide aspects of project design as a technical consultant, providing expertise on complex components of larger projects.
• Regularly engage with the data science community and participate in cross-functional working groups.
• Mentor junior team members and provide training sessions to peers and management.

• 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 Masters degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelors degree(scientific field of study) and a minimum of 5+ years of relevant experience.

• Breadth of knowledge with feature engineering, traditional machine learning, deep learning, and generative AI.
• Familiarity with common machine learning frameworks (e.g., scikit-learn, PyTorch, Hugging Face, etc.).
• Highly skilled in at least one data science discipline (e.g., computer vision, NLP, etc.).
• Curiosity, ability to learn quickly, and a problem-solving mindset.
• Demonstrated ability to collaborate with cross-functional teams, interact with business stakeholders to understand business problems, and explain complex concepts clearly and concisely.
• Knowledge or experience with integrating multiple modalities (e.g., vision, language, audio) for advanced AI solutions.
• Understanding of DevOps principles and MLOps practices to ensure smooth model deployment, monitoring, and continuous improvement.
• Experience contributing to open-source data science and machine learning projects.

• California
• San Francisco
• Los Angeles
• Philadelphia