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Salary
$31k – $60k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 6+ years exp
Overview
Company
Impact
Profile match
Nielsen is a global leader in audience measurement, data, and analytics that shapes the future of media by tracking consumer behavior across all channels and platforms. Founded in 1923, the company is widely recognized for its television ratings system, which provides critical insights into audience size and composition for the media and advertising industries. Today, it operates in over 100 countries, helping clients understand what audiences watch and listen to through comprehensive, independent measurement services.

We are seeking a Senior Data Scientist to bridge the gap between traditional statistical rigor and the frontier of Agentic AI. You will lead the development of intelligent systems that automate complex reasoning while ensuring our underlying data methodologies, specifically around sampling and universe estimation, remain world-class. This role is perfect for a scientist who excels at both deep-math modeling and modern LLM orchestration.

Responsibilities:

  • Agentic AI and GenAI: Design and deploy autonomous AI agents and multi-agent workflows (e. g., using CrewAI, LangChain, or AutoGen) to automate data synthesis and decision-making.
  • Advanced ML and Statistics: Build and scale predictive models using advanced techniques (Gradient Boosting, Deep Learning, Bayesian Inference) to solve non-linear business problems.
  • Sophisticated Sampling: Develop ML-based sampling methodologies (e. g., stratified, PPS, or synthetic population generation) to ensure model training sets are representative and bias-free.
  • End-to-End MLOps: Own the lifecycle from R& D and experimental design to productionizing models in cloud environments (AWS/GCP/Azure).
  • Strategic Roadmapping: Identify and implement high-impact GenAI use cases that transform internal workflows or customer-facing products.

Requirements:

  • Experience: 6+ years of professional experience in Data Science, with a track record of deploying models to production.
  • Generative AI: Hands-on experience with LLMs, RAG architectures, and fine-tuning models for specific domain tasks.
  • Agentic Frameworks: Proven ability to build Agentic systems that utilize tool-calling and autonomous reasoning.
  • Statistical Mastery: Expert-level knowledge of advanced sampling and weighting techniques to handle high-variance datasets.
  • Tech Stack: Expert proficiency in Python, SQL, and ML frameworks (PyTorch, TensorFlow, or Scikit-learn).

Preferred Qualifications:

  • Audience Measurement: Experience with reach/frequency modeling, panel data calibration, or media measurement.
  • Universe Projections: Proficiency in projecting sample data to total population (universe) estimates using census or big-data benchmarks.
  • Domain Expertise: Background in AdTech, Market Research, or Media Analytics.
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