Lead Data Scientist (Machine Learning)

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.
Job Summary
The Strategic Research team within the Nielsen One Data Science organization focuses on expanding and enhancing Nielsen’s digital measurement and optimization products. This branch of the team supports Nielsen ONE Content and Digital in TV Ratings, which provides cross platform deduplication across programs and content distributors and linear TV digital audio measurement, respectively.
The Lead Data Scientist role provides an opportunity to drive methodological innovation in the exciting and fast changing world of media measurement. This position will develop new methodologies and improve existing ones to most optimally measure audiences across TV and/or Digital platforms. This is an ideal position for an experienced researcher to hone their technical leadership skills through the design, creation, and deployment of cutting edge methodologies in audience measurement
In this role, you will work closely with other members of the team to optimize the current codebase, create simulation environments and pipelines, and conduct research that will enhance the utility of our data. In addition, you will have the opportunity to grow your data science skills (coding, statistics) through all aspects of the data science workflow and data product lifecycle. Responsibilities may include the following: methodology development, analysis, model building, automation, creation of robust/scalable code packages, technical oversight, and mentoring. 

Responsibilities

• Lead multiple simultaneous data science projects end-to-end.
• Deploy and maintain data pipelines and models in a production environment.
• Work with cross-functional teams to create, productionize, validate, and optimize methodologies.
• Lead research and analysis to ensure methodologies are fit for purpose and working as intended; troubleshoot, diagnose, and resolve when they are not.
• Develop, implement and drive good programming standards and practices across data science team.
• Drive git and cloud development tools adoption extensively to iterate and improve data science workflows.
• Communicate research findings to varying audiences, including senior Data Science leadership.
• Train and mentor junior data science associates.
• Support Data Science leadership in setting direction for new analytics research and methodology development.
• Partner with cross functional teams (eg Product, Technology, Operations) in resolving critical data issues.

Skills

• Bachelor’s degree or advanced degree (preferred) in a quantitative research field, such as computer science, data science, statistics, mathematics, biological/physical sciences, etc.
• 5-10 years of code development/statistical research experience in a business setting that drives analytic product innovation and implementation.
• Highly proficient in Python, Spark, and SQL.
• Excellent machine learning fundamentals.
• Excellent statistical and analytical skills.
• Excellent software engineering fundamentals and debugging skills.
• Significant experience with collaborative code development, including version control, unit/integration testing, code review and sharing.
• Significant experience working in a cloud-based environment, ideally AWS.
• Very strong communication (verbal and written) and presentation skills in English.
• Very strong critical thinking and problem solving skills.
• Very strong sense of of curiosity, skepticism, and attention to detail.

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