Senior Data Scientist and Modeler

Company Description

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Job Description

The Custom Media Analytics Delivery team sits within Nielsen's Commercial organization and functions
as an incubator for new innovative products. We leverage existing Nielsen datasets to build custom
solutions that don't yet exist in Nielsen's standard product portfolio. That means taking raw, often messy
datasets from across Nielsen's ecosystem and turning them into something a client can actually use —
which requires knowing the data deeply, modeling it correctly, and moving fast.

What You'll Do

• Design and build new measurement products by combining Nielsen datasets across National and Local linear TV, Streaming, Audio, and Digital — understanding the weighting rules, projection logic, and methodology differences that make cross-dataset work hard to get right
• Develop statistical and machine learning models that extend or adapt Nielsen methodologies to answer questions that standard products can't address
• Write production-quality SQL, Python, and PySpark to extract, transform, and model data from

Nielsen's cloud data environment

• Build reusable data pipelines and ETL workflows that allow custom solutions to be delivered repeatably rather than rebuilt from scratch each time
• Use AI tools actively — to validate models, accelerate pipeline development, stress-test logic, and compress the time between concept and delivery
• Translate stakeholder requests into well-scoped analytical problems, push back when the ask is unclear, and deliver with clear documentation of methodology and assumptions
• Collaborate with Research, Commercial Sales, and Client Insights teams; communicate complex model decisions in plain language

Qualifications

Nielsen & Media Research Knowledge

• 3+ years working directly with Nielsen datasets (TAM, DAR/N1Ads, DCR, Audio, or similar) with hands-on knowledge of Nielsen's weighting, projection, and audience estimation methodology
• Proven ability to merge and reconcile multiple Nielsen data sources, navigating differences in sample design, universe estimates, and reporting conventions
• Solid grasp of US media research fundamentals across Television, and Digital

Data Science & Modeling

• Advanced degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field
• 5+ years in data science or analytical research roles, with a track record of delivering production-grade models — not just analyses
• Strong foundations in statistical modeling, sampling theory, weighting, and survey-based projections; comfortable with ML techniques where they fit
• Engineering & Technical Stack
• Expert-level Python and SQL; strong PySpark for big data work in cloud environments (AWS preferred)
• Experience with Databricks for large-scale data processing and ML workflows; familiarity with warehouse-native ML (Databricks ML, Snowflake, or BigQuery ML) is a plus
• Experience building and maintaining ETL pipelines using Airflow or equivalent orchestration tools
• Familiarity with data warehousing concepts, cloud-native storage (Redshift, S3, or similar), and data engineering principles

AI Fluency — Required, Not Optional

• Active daily use of AI tools (LLMs, copilot-style assistants) for code generation, model validation, documentation, and workflow acceleration — this is a core expectation of the role
• Experience designing agentic AI workflows for automation — chaining tools, validation steps, and outputs to reduce manual effort on repeatable tasks
• Comfortable evaluating where AI outputs need verification versus where they can be trusted; understands the limits as well as the leverage

Communication & Delivery

• Ability to explain methodology decisions to non-technical stakeholders without oversimplifying the tradeoffs
• Strong documentation habits — methods, data dictionaries, and assumptions written up so others can reproduce and build on your work
• Proficiency in Tableau, Spotfire, or equivalent visualization tools for QA and client-facing output

This role is for someone who is energized by b