Lead Data Scientist (Model Building Experince Is Manadatory)

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 its 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 Summary

Nielsen is the leading media measurement company, measuring convergent TV, online and radio consumption across the globe. Our Data Science team is experimenting, testing, and implementing innovative methodologies to provide the insights that publishers and advertisers need. As a Data Scientist you will be responsible for high-quality design, execution and delivery of global analytics solutions, performance dashboards, and analytical studies. Excited Come join us!

You will be part of a data science team that is responsible for the Advanced Audience measurement. With the team you will develop new methodologies using techniques from the fields of econometrics and machine learning, such as regression- and classification models, dimension reduction, time series models, nearest neighbour type of algorithms and (convex) optimization. Together you will build and maintain robust pipelines that apply these methodologies. With this, the team supports the advanced analytics needs of Nielsens audience measurement products that provide value for clients. The team collaborates cross functionally with colleagues from product, data engineering, DevOps and other data science teams to develop joint solutions.

In this role your focus in the team will be on methodology development, although you will also take part in the other team tasks, such as data engineering, developing and maintaining production pipelines, and various analytics tasks.

• 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
  

• Possession of a Bachelors 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 whats noise and what isnt
• 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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