MetLife seeks an experienced Lead Data Scientist to drive ML and AI initiatives within the Data and Analytics organization, focusing on marketing campaigns and business engagement. The role combines hands-on technical leadership with ownership of architecture and implementation decisions in a regulated enterprise environment.
Responsibilities
- Lead the solution and a team of data scientists delivering AI and ML solutions for marketing and business engagement use cases.
- Own technical decisions, project outcomes, timelines, and production stability within a defined domain.
- Plan and execute data science use cases, ensuring alignment with business goals and objectives.
- Design, train, and optimize machine learning and deep learning models for marketing and engagement scenarios.
- Analyze complex data sets to identify trends, patterns, and actionable insights informing business strategy.
- Collaborate with stakeholders and cross-functional teams to develop and implement data-driven solutions.
- Enable seamless integration of AI capabilities into business applications and workflows via APIs, SDKs, and microservices.
- 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 Azure cloud tools.
- Research and innovate by staying up to date with advances in AI, data science, and ML.
- Apply ML-Ops best practices to optimize platform components for efficiency, scalability, and reliability.
Requirements
- 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 or financial industry experience with sales, marketing, and customer engagement analytics.
- Proven experience designing, deploying, and operating production ML and/or GenAI solutions, including APIs, batch, and real-time inference.
- Experience developing machine learning models using Python, preferably in the cloud.
- Familiarity with responsible AI practices, including data privacy, bias mitigation, and model monitoring.
- Strong SQL knowledge and data analysis skills for anomaly detection and exploratory data analysis.
- Experience with Dominos, Power BI, and/or Azure ML.
- Solid understanding of statistics and mathematics essential for data analysis and prediction.
- Experience applying predictive modeling or AI solutions to improve customer experience, revenue generation, ad targeting, and other business outcomes.
- Excellent presentation skills to convey results with storytelling, visualizations, and clear conclusions.
- Strong problem-solving abilities and effective written and verbal communication skills.
Technologies
- Python
- Azure ML
- Power BI
- Dominos
- Databricks
- SQL
- Azure
Benefits
- Medical/prescription drug and vision coverage
- Dental insurance
- Short-term and long-term disability (no-cost)
- Company-paid life insurance and legal services
- Retirement pension funded entirely by MetLife
- 401(k) with employer matching
- Group discounts on voluntary insurance products (auto and home, pet, critical illness, hospital indemnity, and accident insurance)
- Employee Assistance Program (EAP) and digital mental health programs
- Parental leave
- Paid time off
- Paid holidays
- Volunteer time off
- Tution assistance
Role value proposition
The position sits within MetLife's newly consolidated Data and Analytics organization supporting the U.S. business. Data and Analytics assists MetLife's U.S. lines with data infrastructure, governance, engineering, modeling, analysis, and AI across data, business intelligence, and data science domains. The Lead Data Scientist plays a critical role in the Engagement Strategy team, delivering ML and AI solutions to support marketing campaigns and customer engagement while providing hands-on technical leadership in a regulated enterprise.
You will own the technical architecture and implementation decisions for solutions within a defined business domain, ensuring scalability, reliability, and governance-aligned delivery. Collaboration with architects, data engineering, platform engineering, DevOps, product, and business stakeholders translates requirements into robust AI solutions.
Location and hybrid work
This is a hybrid role requiring a minimum of 3 days per week in the office.
Preferred qualifications
- Experience with employee benefits plans is a plus.
- Hands-on experience with cloud platforms such as Azure and 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 like document Q&A, summarization, chatbots, and knowledge retrieval.
