Assistant Director, Data Science (STP)
The Liberty Mutual, Global Risk Solutions, Data Science Solutions is seeking an Analyst II/Assistant Director of Data Science within the Analytics Transformation team. The Analytics Transformation team is responsible for championing the future of Data Science, helping to incubate new ideas and encourage innovation with purpose across Global Risk Solutions, with a strong focus on leveraging Artificial Intelligence (AI) and Machine Learning (ML) to drive business value. The team is looking for a passionate individual who is full of curiosity, open to new ideas, and willing to fail fast and embrace a culture of experimentation. This individual will join our Agile team and play an essential role in building out in-house AI/ML capabilities and be involved in the end-to-end product cycle, from ideation to model development/deployment to field implementation.
This individual will need strong experinence building deep learning computer vision and NLP models as well as building end to end data science pipelines through MLOPS. This is an individual contributor role.
Responsibilities:
- Buildout in-house AI/ML capabilities with other team members, including data scientists, IT developers, and business stakeholders
- Research and develop computer vision, NLP, and other AI/ML models to improve operational efficiency, risk selection, claims response and pricing
- Collaborates with business partners to develop data science solutions that enable data-driven strategic decision-making
- Utilizes data science techniques to manipulate structured and unstructured data sets, identify patterns in raw data, and develop models to optimize business solutions.
- Collaborate with other data science teams across the company to promote best practices and reproducible work
- Act as a mentor and help develop less experienced analysts or interns on the team
Qualifications:
- Bachelors w/ 5+ years, Masters w/ 4+ years, or Ph. D with 2+ years of relevant experience.
- Advanced degree in quantitative field or actuarial credentials preferred.
- Experience with predictive modeling and statistical software packages (e.g., Python/R) highly desired.
- Proven ability to quickly grasp new concepts and technologies and adapt to changes and demands.
- Expert skills in Excel, PowerPoint highly desired.
- Ability to exchange ideas and convey complex information clearly and concisely, both verbally and in writing.
Qualifications
- 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.
At Liberty Mutual, our purpose is to help people embrace today and confidently pursue tomorrow. That's why we provide an environment focused on openness, inclusion, trust and respect. Here, you'll discover our expansive range of roles, and a workplace where we aim to help turn your passion into a rewarding profession.
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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.