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.
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
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