Assistant Director, Data Science (STP)

Assistant Director, Data Science (STP)

Liberty Mutual

Boston, MA

Job Description

Collaborates with business partners to develop predictive analytic solutions that enable data-driven strategic decision-making.

Utilizes data science techniques to manipulate large structured and unstructured data sets, identify patterns in raw data, and develop models to predict the likelihood of a future outcome and/or to optimize business solutions.

Applies knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets to generate insights to inform business decisions.

Identifies and tests hypotheses, ensuring statistical significance, as part of building and developing predictive models for business applications.

Translates quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data.

Enables the business to make clear trade-offs between and among choices, with a reasonable view into likely outcomes.

Customizes analytic solutions to specific client needs.

Responsible for major components of high complexity projects.

Regularly engages with the data science community and participates in cross-functional working groups.

Domestic travel required up to 5%.

Telecommuting permitted up to 60%.

Qualifications

Employer will accept a Bachelor's degree (or foreign equivalent) in STEM, Industrial Engineering or related field and 5 years of experience in the job offered or in an Assistant Director, Data Science (STP)-related occupation. Position also requires demonstrable experience in the following:

• Researching and adapting statistical and machine learning methods for business related modeling use.
• Utilizing predictive analytic techniques and toolsets and statistical diagnostics of models.
• Building predictive models using techniques including GLMs and machine learning models.
• Interrogation of machine learning models using contribution methods including Shapley values.
• Object Oriented Programing in python and ML Ops practices.
• Package development for predictive modeling or statistics including python packages or R packages.
• Demonstrated good communication skills to exchange ideas and convey complex information clearly and concisely.
• Demonstrated ability to quickly learn business and market context and learn subject experts' insights for modeling purposes.
• Building insurance pricing models.
• Deploying machine learning models for pricing.
• Domestic travel required up to 5%.
• Telecommuting permitted up to 60%.