Senior Data Scientist - End-to-End Analytics Lead (Hybrid)

New York Life Insurance Co; Tampa (FL); On-site; USD 80,000 - 110,000; Full time

Job summary

New York Life Direct in Tampa, Florida seeks a Senior Associate, Data Scientist to lead data-driven insights across marketing, underwriting, actuarial, and fraud prevention. You will own data science projects end-to-end—from framing problems to deploying models—working with engineers and business partners to deliver measurable impact.

You should have a strong quantitative background, experience applying data science in business, and fluency with Python or R and SQL.

Qualifications

• Master’s degree in a quantitative field required.
• 1–3+ years applying data science/analytics in business settings.
• Proficiency with Python and/or R; familiarity with SQL.
• Strong foundation in statistics, ML and data analysis.
• Ability to translate data into actionable insights.
• Strong communication to influence stakeholders.
• Ability to work independently and manage priorities.

Responsibilities

• Own data science projects across full lifecycle: framing, exploration, feature engineering, modeling, evaluation, delivery.
• Apply statistical and ML methods to analyze structured/unstructured data.
• Translate business questions into analytical frameworks for clear recommendations.
• Develop, test, and deploy models with engineering collaboration.
• Identify opportunities to improve processes and model performance.

Skills

Python language

R language

Statistics & ML fundamentals

Machine learning concepts

Communication skills

Independent work

Problem solving

Education

Master’s degree in quantitative field

Tools

SQL

AWS/Sagemaker

Job description

New York Life Direct in Tampa, Florida seeks a Senior Associate, Data Scientist to lead data-driven insights across marketing, underwriting, actuarial, and fraud prevention. You will own data science projects end-to-end—from framing problems to deploying models—working with engineers and business partners to deliver measurable impact.

You should have a strong quantitative background, experience applying data science in business, and fluency with Python or R and SQL.