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# Senior Associate, Data Scientist

- **Company:** New York, Inc.
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Salary:** $80,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Cloud Services, Standard Sql, SQL Databases, Unstructured Data, Data Processing, Feature Engineering, Model Validation, Information Technology, Data Analytics, Programming Languages
- **Published:** October 2, 2026
- **Apply:** https://www.careerjet.com/job/use7fa17b729f7b3b387a56af86af2534c/eaa

## About the Role

You bring a strong academic foundation in a quantitative field such as Data Science, Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related discipline, along with professional experience applying data science or analytics to solve a broad range of business problems. You have working knowledge of programming languages such as Python or R and familiarity with SQL, along with a solid grounding in statistical analysis, machine learning concepts, and data manipulation. You are comfortable working with real-world datasets and can independently translate data into meaningful, actionable insights. You communicate clearly and effectively with both technical and non-technical audiences and can influence stakeholders through data-driven recommendations. You demonstrate sound judgment, attention to detail, and the ability to manage multiple priorities, along with a strong commitment to continuous learning., * Master's degree in a quantitative field (or equivalent experience)

 * Typically, 1-3+ years of experience applying data science or analytics in a business or applied setting
 * Experience with Python and/or R, and familiarity with SQL
 * Strong foundation in statistics, machine learning, and analytical problem-solving
 * Demonstrated ability to independently execute end-to-end analytical projects
 * Experience working with real-world datasets and delivering actionable insights
 * Strong communication skills with the ability to influence both technical and non-technical stakeholders
 * Ability to work independently with minimal supervision and manage competing priorities

Preferred Skills:

 * Exposure to experimentation, model evaluation, or model monitoring
 * Experience in financial services, insurance, or direct-to-consumer businesses
 * Familiarity with cloud-based tools and platforms (e.g., AWS, Sagemaker)
 * Experience applying generative AI in business settings

## Description

As a Senior Associate, Data Scientist on the Advanced Analytics team, you will lead and deliver data-driven insights and analytical solutions that support key business initiatives across New York Life Direct. Working as part of a collaborative team, you will apply statistical analysis, experimentation, and machine learning techniques to solve real-world business problems across marketing, underwriting, actuarial, operations, and fraud prevention. In this role, you will independently translate business questions into analytical frameworks, own the delivery of scalable analytics solutions, and contribute to the team's overall impact.
What You'll Do:
You will own data science projects and/or key analytical workstreams across the full lifecycle, including problem framing, data exploration, feature engineering, model development, evaluation, and communication of results. You will work be able to work independently but will also have access to a highly collaborative and experienced team to help tackle complex or ambiguous challenges. You will apply statistical and machine learning methods to analyze structured and unstructured data, generating insights that directly inform business decisions and strategies. Working with cross-functional stakeholders, you will proactively define business problems and translate them into analytical approaches, delivering clear, actionable recommendations. You will lead the development, testing, and deployment of models in partnership with engineering teams, applying best practices in model lifecycle management. Additionally, you will identify opportunities to improve processes, enhance model performance,
and drive efficiency.


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