Senior Data Analyst (with LLM experience) | Mercer

We’re looking for a Senior Data Analyst with handson experience in Python and LLMs to join our internal data enablement team. This is a highimpact role that works across multiple product areas — not embedded in a single squad — helping teams uncover insights, structure their data effectively, and explore new opportunities using Large Language Models.

If you enjoy combining analytical depth with business context, love collaborating across functions, and are excited to work with emerging AI technologies, this role could be a great fit.

Responsibilities:

• Validate Business Assumptions: Analyze and test key project assumptions to support reliable decisionmaking

• Perform Deep Data Analysis: Extract meaningful insights from complex datasets across products and teams

• LLM Feature Development: Contribute to the design and implementation of product features using Large Language Models (e.g., OpenAI, Hugging Face)

• Data Structuring & Guidance: Advise teams on data structuring for optimal analytical use

• Documentation: Create clear, practical guidelines and documentation for data usage, pipelines, and insights

• Stakeholder Collaboration: Engage with product managers and crossfunctional stakeholders to align data strategies with business goals

• Essential knowledge, skills & experience:

  • 5+ years in a datafocused role (e.g., Data Analyst, Data Scientist, Analytics Consultant)

  • Proficiency in SQL for querying large datasets

  • Familiarity with Python and R for data analysis and machine learning.

  • Familiarity with LLM frameworks such as OpenAI or Hugging Face

  • Experience with data visualization tools such as Tableau or Power BI

  • Solid understanding of data modeling, exploratory data analysis, and statistical testing

  • Excellent communication skills for technical and nontechnical stakeholders

  • Nicetohave:

    • Experience in the HR, people management, or compensation domains

    • Experience working in distributed, crossfunctional product teams

    • Knowledge of MLOps best practices or experience deploying data science models to production

    • Interview Process

      1. Interview with GT Recruiter

      3. Technical Interview

      5. Competency interview

      7. Final Interview

      9. Reference Check