Lead Data Scientist

MetLife; Bridgewater (MA); On-site; USD 160,000 - 220,000; Full time

Job summary

MetLife seeks a Lead Data Scientist to guide AI/ML initiatives across Marketing and Business Engagement. You will own technical architecture, model development, and deployment in a regulated enterprise environment.

You will collaborate with data engineering, platform teams, and stakeholders to deliver scalable, governance‑compliant AI solutions and drive measurable business outcomes.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
• 8+ years of AI/ML engineering and/or data science experience.
• 5+ years in insurance/financial industry analytics (sales/marketing/customer engagement).
• Proven experience designing, deploying, and operating production ML/GenAI solutions with APIs, batch, and real‑time inference.
• Experience developing ML models using Python (cloud preferred).
• Familiarity with responsible AI, data privacy, bias mitigation, and/or model monitoring.
• Strong SQL and data analysis skills for anomaly detection and exploration.
• Experience with Power BI, Azure ML, and Databricks.

Responsibilities

• Lead the solution and a team of data scientists delivering AI/ML solutions for marketing and engagement use cases.
• Own technical decisions, project outcomes, timelines, and production stability within a domain.
• Plan and align data science use cases with business goals and objectives.
• Design, train, and optimize ML/DL models for marketing and engagement use cases.
• Analyze complex data sets to identify trends and actionable insights for strategies.
• Collaborate with stakeholders to develop and implement data‑driven solutions.
• Enable integration of AI capabilities into business apps via APIs, SDKs, microservices.

Skills

Python

SQL

Azure

Data Science

Machine Learning

Leadership

Communication

GenAI

Education

Bachelor's or Master's in CS/Data Science

Tools

Power BI

Databricks

Azure ML

Job description

Role Value Proposition

The position sits within the newly consolidated Data and Analytics (D&A) organization supporting the U.S. Business of MetLife. U.S. D&A assists all business lines of MetLife's U.S. business (about 2/3 of MetLife Global by earnings) with everything related to data, analytics, and data science, from data infrastructure, data governance, data engineering, data modeling, data analysis, to business intelligence, data science, and AI. The Lead Data Scientist is crucial to DnA USB's Engagement Strategy team, creating Machine Learning and AI solutions to support marketing campaigns and business engagement. You will provide hands‑on technical leadership in the design, development, and operation of Machine learning and AI solutions within a regulated, enterprise environment. You will own technical architecture, solution, and implementation decisions for solutions within a defined business domain, ensuring solutions are scalable, reliable, and compliant with governance and risk standards. You will work closely with the architect, data engineering, platform engineering, DevOps, product, and business stakeholders to translate business requirements into robust AI solutions.

Key Responsibilities

• Team Leadership: Lead the solution and a team of data scientists delivering AI and ML solution for marketing and business engagement use cases
• Ownership: Accountability for technical decisions, project outcomes, timelines, and production stability within a defined domain.
• Planning and Business alignment: Lead the planning and execution of data science use cases, ensuring alignment with business goals and objectives.
• Model Development: Design, train, and optimize machine learning and deep learning models for a variety of marketing and business engagement use cases
• Data Analysis: Analyze complex data sets to identify trends, patterns, and actionable insights that can inform business strategies.
• Collaboration: Collaborate with stakeholders and cross‑functional teams to develop and implement data‑driven solutions.
• Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Stakeholder Communication: Visualize data, create reports, and present findings to senior management and cross‑functional teams.
• Develop statistical models, analytics, and Machine Learning algorithms using Python and cloud tools (Azure).
• Research and Innovation: Stay up to date with the latest advances in AI, Data Science, and Machine Learning.
• ML‑Ops Best Practices: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud‑native architectures.

Required

Essential Business Experience and Technical Skills

• Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• 8+ years of overall experience in AI/ML engineering and/or data science.
• 5+ years of insurance business and/or financial industry experience with sales, marketing, and/or customer engagement analytics.
• Proven experience designing, deploying, and operating production ML and/ or GenAI solutions, including APIs, batch, and real‑time inference.
• Experience in developing Machine Learning models using Python (preferably in the cloud).
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and/or model monitoring.
• Strong SQL knowledge and data analysis skills for data anomaly detection and Exploratory Data Analysis.
• Experience with Dominos, Power BI, and/or Azure ML.
• Statistical Knowledge: A strong understanding of statistics and mathematics is essential for data analysis and prediction.
• Use predictive modeling or AI solutions to increase and optimize customer experience/communication, revenue generation, ad targeting, and other business outcomes.
• Very good presentation skills to present results clearly and effectively by creating presentations with storytelling, visualizations & results.
• Very good problem solver and excellent communication skills — both written and verbal.

Preferred

• Experience with employee benefits plans is a plus.
• Hands‑on experience with cloud platforms (Azure/Databricks).
• Hands‑on expertise with Retrieval‑Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs.
• Strong understanding of prompt engineering, fine‑tuning, and evaluation of generative models for real‑world applications.
• Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval.

At MetLife, we’re leading the global transformation of an industry we’ve long defined. United in purpose, diverse in perspective, we’re dedicated to making a difference in the lives of our customers.