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
This role may have in-office requirements based on candidate location
Come shape the future of distribution at Liberty Mutual! The Distribution Data Science team spans all brands and lines of business within USRM, and the variety of our work reflects this breadth of responsibility.
We currently own a wide range of modeling solutions driving sales efficiency and are continuing to expand our footprint with innovative data science solutions.
Help us build a roadmap to drive sales excellence using the latest modeling techniques such as Double ML, LLaMA and GenAI solutions.
We are seeking an inventive and self-motivated individual to help transform insurance distribution. In this role you will experience the entire data science lifecycle.
You will partner closely with business stakeholders to research and identify opportunities, build cutting edge data science products, and collaborate with ML Engineers to deliver your projects to production.
The distribution landscape is changing quickly! To achieve success in this role, a candidate will need to be able to adapt to change and have a curious mind.
This is a highly technical position and will be a great fit for someone with a desire to create and a drive for constant improvement.
A successful candidate will :
- Have experience building and publishing well documented libraries and packages.
- Be comfortable developing automated ML pipelines.
- Have experience building a wide range of models, GBM, Uplift, NN.
- Have the ability to collaborate with non-technical stakeholders, engineers, and fellow data scientists to bring complex ideas to market.
In addition to the responsibilities below, you will :
- Collaborate with business partners to design data driven solutions and implementation strategies
- Develop complex science-driven data products such as models, dashboards, and datasets.
- Manage the development lifecycle of the products, including deployments and the CI / CD pipeline.
- Develop and enhance MLOps best practices for data and model deployments.
- Partner with Engineering leaders to develop best-in-class processes for bringing complex model features to market.
- Present findings, share insights, and make recommendations that impact profitability and growth.
- Provide tactical as well as strategic input on highly technically complex projects that drive change across function, SBU or Corporate Department.
- Regularly engage with the data science community and support cross-functional working groups.
Responsibilities :
- Researches and develops predictive analytic tools.
- Leverages knowledge to create / design solutions for business needs.
- Mines large data sets using sophisticated analytical techniques to generate insights and inform business decisions.
- Identifies and tests hypotheses, ensuring statistical significance, and builds predictive models for business application.
- 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 larger components of projects of moderate to high complexity.
- Guides aspects of project design as a technical consultant for the team.
- Regularly engages with the data science community and participates in cross functional working groups.
Qualifications
Broad knowledge of predictive analytic techniques and statistical diagnostics of models. Deep theoretical and practical understanding of deep learning models.
Expert knowledge of predictive toolset; reflects as expert resource for tool development. Strong programming and MLOps skills are required, including reviewing and suggesting improvements to other team members’ code.
Experience using a version control system such as Git is required.
Experience building and maintaining a production-grade model pipeline using modern tools is desired.
Experience building and published shared packages is required.
Experience interacting with Snowflake or other Cloud-based RDMS, and dashboarding using Datadog is required.
Familiarity with Airflow, Docker, and CI / CD build pipelines is preferred. Experience with building, maintaining, and scaling pipelines with orchestration tools such as Airflow and or Luigi is required.
Expertise with Python is required.
- 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.
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