Lead Data Scientist

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. #J-18808-Ljbffr MetLife