Senior Director I, Modeling Data Pipelines

Liberty Mutual Insurance•Boston, MA

1d•$179,000 - $322,000

About The Position

Leads development and delivery of end-to-end data pipelines and feature store capabilities that provide data scientists with modeling data at the speed required by P&L vertical teams. Provides strategic and tactical direction for the department and helps determine how MDS, data scientists, and deployment teams across US Data Science (USDS) work with modeling data. The role requires proven ability to lead large-scale change across multiple teams. Manages, provides technical oversight for, and develops a team of data analysts, MLOps specialists, and subject matter experts. Owns the strategy and execution for modernizing and standardizing the modeling data lifecycle, from data ingestion through model deployment, in partnership with P&L vertical data science, modeling data, and deployment teams. Uses modern pipeline frameworks and scalable architecture patterns to track data lineage throughout feature development. Leads the design and adoption of a feature store strategy that defines features once, manages them consistently across model development, calibration, and deployment, and allows data scientists to add features directly. Uses pipeline modernization to develop AI solutions that accelerate how features are built, tested, and maintained. Requires deep knowledge of modern data pipelines and feature management practices, including tools such as dbt, Snowflake, and Databricks, and their advanced application to pricing model development, calibration, and deployment. Collaborates with USDS, Data Office, DAE, ED&DS, and Product Delivery to develop these solutions.

Requirements

Typically 8 or more years of relevant experience, including leadership experience in data science, analytics, data operations, or MLOps.
Experience sourcing, integrating, and governing data from enterprise data warehouses and external vendors.
Ability to partner with data science, technology, and business stakeholders and communicate clearly with technical and non-technical audiences.
Deep knowledge of modern data pipelines and feature management practices.
Experience with tools such as dbt, Snowflake, and Databricks.
Ability to apply data pipeline and feature management practices to pricing model development, calibration, and deployment.
Experience managing, providing technical oversight for, and developing data science and advanced analytics staff, potentially including subordinate managers.
Ability to work on diverse-scope issues requiring evaluation of multiple factors and understanding of current business trends.
Ability to develop highly technical, individualized tools with transformative impact on aligned business units.
Ability to lead complex, technical, and cross-functional projects and assignments.

Nice To Haves

Advanced degree in Statistics, Mathematics, Data Science, Computer Science, or a related field.

Responsibilities

Lead development and delivery of end-to-end data pipelines and feature store capabilities for modeling data.
Set strategic and tactical direction for the department and help guide how USDS teams work with modeling data.
Manage, provide technical oversight for, and develop a team of data analysts, MLOps specialists, and subject matter experts.
Own the strategy and execution for modernizing and standardizing the modeling data lifecycle from ingestion through model deployment.
Partner with P&L vertical data science, modeling data, and deployment teams on lifecycle modernization.
Use modern pipeline frameworks and scalable architecture patterns to track data lineage throughout feature development.
Lead the design and adoption of a feature store strategy that ensures features are consistently defined and managed across model development, calibration, and deployment, and can be added directly by data scientists.
Leverage pipeline modernization to develop AI solutions that accelerate feature building, testing, and maintenance.
Collaborate with USDS, Data Office, DAE, ED&DS, and Product Delivery to develop solutions.
Coach the team to understand organizational vision and goals.
Collaborate with business partners to develop tools and services such as modeling, dashboards, and decision aids for strategic decision-making.
Own relationships with a defined business segment and deliver advanced tools and insights, including ongoing monitoring of metrics and initiatives.
Lead complex, cross-functional projects that drive change across a function, SBU, or corporate department.
Direct and may perform complex predictive analytics projects and cross-functional assignments in collaboration with multiple functions.
Shape the data science community and broader audiences, and lead cross-functional working groups.