Job 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.

Data Scientist

Nielsen is the largest measurement company in the world with unique measurement technologies, assets, and data that make it one of the most interesting and challenging places for a data scientist to work. We focus on what consumers watch, listen to, and buy in over 95 countries.

Data Science is core to what Nielsen does, and our research projects have high visibility in directly affecting the results of our business and our clients. This Data Scientist role in the Marketing Cloud team provides an opportunity to contribute to methodological innovation in the exciting and fast-changing world of digital media measurement. This is an ideal position to grow as a model builder, developer, researcher, and to contribute to innovative products. The Marketing Cloud team within the Analytics Portfolio organization focuses on creating a holistic view of individuals and households across all channels and devices, unifying online browsing behaviors, offline panel data, mobile device usage, as well as linear and digital television viewership. Using our expertise in big data, analytics, and machine learning, we enable marketers to engage individuals and households with personalized messaging, drive performance at scale, and holistically measure marketing effectiveness. As part of this exciting team, this position will focus on developing new methodologies, data mining and predictive modeling, and the automation of our modeling processes

• This position requires a detail-oriented person who has experience in big data analysis using multiple data sources and statistical research, and who enjoys working in a fast-paced environment. Ability to problem-solve, work independently on critical initiatives and see the big picture are keys to success in this position.
• Master’s in statistics, quantitative social sciences, economics, operations research, or hard sciences (e.g. engineering, computer science, biology, physics, etc.) with outstanding analytical expertise and strong technical leadership
• OR 1-2 years of work experience focusing on the following:
• Creating, organizing, analyzing, and correcting very large datasets using statistical models
• Coding in data science-related programming languages; required experience with Python (numpy, pandas, sci-kit learn, etc.)
• Proficiency in SQL & big data technologies.
• Leading and managing complex projects with multiple stakeholders
• Excellent communication & presentation skills (written and verbal)
• Experience in media or marketing analytics, e.g. lookalike modeling, insights analysis, customer segmentation
• Ability to work independently and solve complex problems
• Naturally curious, has a passion for solving problems and critical thinking
• Good vibes, integrity, and good work ethic

• Experience with online media and the ad tech ecosystem
• Experience working with global cross-functional teams of various sizes
• Experience with machine learning techniques
• Experience working with cloud-based computing and storage solutions, preferably AWS
• Working knowledge of Bash and Git
• Experience with other tools common to the data science world such as Airflow, Spark, MLlib, MLflow, Tensorflow, and PyTorch
• Experience with data visualization tools (e.g. Superset)

• Build, evaluate, and maintain propensity models at scale
• Write production level code that integrates seamlessly into already productized model building pipeline
• Develop and implement new machine learning techniques to improve performance of client facing models in production.
• Automate model surveillance and maintenance in order to streamline modeling system.
• Research and develop new use cases for Nielsen Identity data assets
• Support the business and client teams by investigating complex analytical challenges.
• Stay up to date on industry changes to digital measurement (e.g., new devices and platforms, privacy laws updates, changes in browser/app measurement, etc.)and critically assess how it would impact Nielsen measurement.
• Engage in discussions on strategic direction of product from a client perspective.
• Stay informed of new research and developments in the field
• Confidently represent Data Science methods and approaches to internal and external partners and clients.
• Participate in internal and external knowledge exchanges (conferences, workshops, webinars).

Nielsen: Enabling your best to power a better media future. Our comprehensive benefits package (including health & wellness plans for full-time employees, 401(k) retirement coupled with a Nielsen match, a generous paid time off policy, and if eligible, a discretionary incentive/bonus) is designed to be inclusive for all employees and families, and we take pride in ensuring that employees are rewarded holistically for the role they are doing and their performance. 

A reasonable estimate of salary range for a new employee to be offered this role would be between $38,000-$155,000 which would be adjusted based on each employee's geographic location. The position of each employee within a compensation range at Nielsen is dependent on several individual circumstances, such as experience, training, certifications and other business requirements/needs

Nielsen is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, protected veteran status or any other protected class.

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