Nielsen

Principal Data Scientist

Job description

Our business is centered on data, because it’s what our clients need to make confident decisions that shape the future of media. Our Data Science team delivers essential metrics and insights to brands around the world through new product ideation, experimental design and complex analysis.


Team: Outcomes Cross Product R&D team


The Outcomes Cross Product R&D team is a newly formed team focused on building Nielsen Audience Outcomes' methodological connections with Audience Measurement. Audience Outcomes is our marketing effectiveness suite of products, which delivers the intelligence advertisers need to evaluate and optimize the impact of their marketing on their short-term and long-term goals. Audience Measurement provides cross-media measurement for advertisers, agencies, publishers, and ad platforms. Building a connection between these two product portfolios will require analyzing the current products, assessing underlying data assets, refining our methodologies to better align with measurement products, and developing shared metrics between media sellers and buyers. The overarching goal of this team is to enable Outcomes transactions’ data and analytics to seamlessly merge into Nielsen ONE.


Objectives of the role:


Grow and lead a small team of data scientists who will be working on the development of the next generation of transactional marketing effectiveness metrics. Research and build the analytical and statistical models required to support those metrics. Partner with the technology teams to bring the whole system into production.


Responsibilities of the role:


Work as part of an Agile cross-functional Scrum team to research, productionalize, validate, and optimize new methodologies and metrics.


Work closely together with the Product Owner to help inform and translate product requirements into technical tasks for the data science team.


Lead and participate in the development and maintenance of a production-level codebase, assessing the value of new technologies for current or future applications.


Summarize, document, and communicate research findings to audiences of varying levels of technical subject matter expertise.


Provide support for technical guidance and training of other team members.


Manage more junior data scientists on the team.


Skills and Qualifications:


5+ years of professional work experience in Statistics, Data Science, and/or related disciplines.


Master’s degree in a quantitative research field, such as data science, statistics, mathematics, social sciences, biological/physical sciences, computer science, etc, or equivalent work experience.


Solid understanding of statistics and probability theory with real-world experience applying that knowledge in analytical problem-solving.


Several years of programming experience in Python.


Expertise with data analysis tools and libraries, such as NumPy / SciPy / scikit-learn / pandas / matplotlib, etc.


Experience with cloud environments and big data technologies (e.g., Spark, AWS)


Collaborative code development experience, including version control, unit/integration testing, code review, and sharing.


Experience in managing and executing large-scale, complex technical projects


Excellent written and verbal communication skills.


Additional advantageous knowledge and experience:


  • Knowledge of Bayesian inference and probabilistic programming.
  • Knowledge of workflow management tools such as Airflow, Luigi, etc.
  • Experience working in an Agile cross-functional development team.
  • Experience managing direct reports.
  • Experience in marketing/media, including domain expertise in any of the following areas:
  • Marketing effectiveness measurement (e.g. MTA, Marketing Mix Modeling, Sales Lift measurement)
  • Audience measurement and Targeting (e.g. reach and frequency measurement, audience segmentation, programmatic activation)
  • Digital / Cross-Platform advertiser and activation (e.g. orchestrating digital ad buys, executing digital / cross-platform campaigns, digital ad operations)
  • Strategic and tactical media planning (e.g. budget allocation, building cross-platform or channel-specific media plans)
  • Technical knowledge of cross-platform media/behavior measurement technologies and data, including any of the following:
  • Media panels
  • Ad serving technology (e.g. JS / pixel tags; data management platforms)


“Big data” media measurement sources (e.g. set-top box, Smart TV data)


  • Data matching methods (e.g. cross-device data; online-to-offline, onboarding; PII matching and management)
  • Secure computing environments (e.g. clean rooms, Ads Data Hub, etc.)

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