Data Scientist II: Growth, AI & Analytics

MetLife; Tampa (FL); Hybrid; USD 90,000 - 115,000; Full time

Job summary

MetLife is hiring a Data Scientist II to design and implement scalable data science, ML, Generative AI, and advanced analytics solutions driving business value. You will manage the full analytics lifecycle—from data acquisition to model deployment and responsible AI practices.

The role emphasizes production-grade AI using modern technologies, collaboration across functions, and adherence to governance and MLOps standards. Hybrid in-office work is expected in the U.S.

Qualifications

• Bachelor’s degree in a quantitative field (Math, Stats, OR related).
• 3–5 years of experience in data science, analytics, ML, or related disciplines.
• Hands-on experience with Python, SQL, statistics, hypothesis testing, feature engineering, and predictive modeling.
• Experience with one or more ML frameworks (Scikit-learn OR TensorFlow OR PyTorch).
• Foundational knowledge of Generative AI including LLMs, prompting, embeddings, vector stores, and RAG.

Responsibilities

• Design, build, validate, deploy, monitor, and optimize analytics, ML, and AI solutions.
• Data extraction, SQL development, data prep, exploratory analysis, feature engineering, model evaluation.
• Develop production-grade predictive and Generative AI solutions using structured and unstructured data.
• Support foundation model adaptation, prompt engineering, embeddings, vector databases, RAG-based AI applications, and agentic AI workflows.
• Utilize Databricks, Apache Spark, and cloud analytics environments.

Skills

Python

SQL

Statistics

Hypothesis testing

Feature engineering

Predictive modeling

Education

Bachelor's degree in Mathematics, Statistics, OR related quantitative field

Tools

Databricks

Apache Spark

Cloud platforms

Job description

MetLife is hiring a Data Scientist II to design and implement scalable data science, ML, Generative AI, and advanced analytics solutions driving business value. You will manage the full analytics lifecycle—from data acquisition to model deployment and responsible AI practices.

The role emphasizes production-grade AI using modern technologies, collaboration across functions, and adherence to governance and MLOps standards. Hybrid in-office work is expected in the U.S.