Lead Data Scientist (Only Chicago - IL) @ NielsenIQ
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Company Description
NIQ is the world’s leading consumer intelligence company with a global, diverse team passionate about revealing the complete consumer journey. With a recent combination with GfK, NIQ offers unparalleled global reach and consumer insights.
Job Description
As a Lead Data Scientist, you will design and implement advanced statistical methodologies including sampling, calibration, projection, and panel data modeling to enhance our consumer panel products. You will collaborate cross-functionally, prototype and deploy statistical solutions, and clearly present findings and recommendations.
- Advance the statistical rigor of our methodologies.
- Lead the development and refinement of models for panel data.
- Collaborate with cross-functional teams to align methodologies with product goals.
- Support deployment of enhancements and new solutions.
- Document findings, methodologies, and best practices.
Qualifications
Strong foundation in statistics (sampling theory, calibration and projection techniques, panel data modeling), ability to manipulate and analyze large data sets, and proficiency in Python, SQL, and large-scale databases. Excellent communication and collaboration skills are essential.
Nice-to-Have Skills
Experience with version control systems, familiarity with cloud computing technologies (e.g., Azure, Linux), and an interest in supporting technology teams in production deployment.
Additional Information & Benefits
- Market-competitive salary with base compensation around $100,000 to $120,000.
- Potential Sales-based or performance bonuses.
- Flexible working environment, comprehensive health insurance, industry-leading parental leave, life insurance, education support, and more.
Key skills/competency
Lead Data Scientist, statistics, sampling, calibration, projection, panel data modeling, Python, SQL, deployment, cross-functional, documentation
How to Get Hired at NielsenIQ
🎯 Tips for Getting Hired
- Research NIQ's culture: Study their global reach and innovation.
- Customize your resume: Highlight statistical expertise and technical skills.
- Showcase project work: Provide examples of data modeling projects.
- Prepare for interviews: Review cross-functional collaboration examples.