Responsibilities
- Explore and analyze data.
- Craft scalable, interpretable models, employing techniques from the fields of statistics, econometrics and machine learning.
- Provide research support for the identification and implementation of methods and best practices.
- Design, develop, optimize, and maintain robust data science pipelines.
- Explain complex analytics methodologies to internal and external Nielsen stakeholders, demonstrating their direct relevance to strategic audience measurement goals.
- Ensure documentation of new methodologies and best practices.
- Help incubate a forward-thinking culture. This includes prioritizing staying up-to-date with new software, methodologies, and industry research and facilitating the cross pollination of ideas across individuals and teams.
- Cultivate a team environment focused on continuous learning
Qualifications
- Possession of a Bachelor’s degree, with a preference for an MS or PhD in Data Science, Computer Science, Statistics or Mathematics, Operation Research, (or other hard sciences,) with outstanding analytical expertise and strong technical leadership skills.
- At least 5 years of experience in data science roles, or a graduate degree in a quantitative discipline with a demonstrated skill set in data science.
- Experience in media industry is good to have.
- You comprehend complex problems quickly, are creative in finding solutions and are a critical thinker with a keen eye for detail.
- You are a team player, you like to test ideas with colleagues and you like to participate in peer reviews.
- Professional expertise in using machine learning and mathematical modeling.
- High proficiency in Python, SQL, and PySpark.
- Expertise in working with big data.
- Have a thirst for new challenges. The candidate should be comfortable diving into the unknown and making sense of what's noise and what isn't.
- Feel comfortable and excited to bring innovative ideas and challenge existing methods and processes. This requires the ability to pick apart the implementation details from the overall design.
- Have a mathematical and/or statistical background with the ability to dive "into-the-weeds" when necessary to help fine-tune methods and models.
- Be able to communicate effectively to various types of personas: commercial partners, product leaders, infrastructure architects, data scientists, and data engineers.

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