Data Scientist | ML

Data Science is at the core of Nielsen’s business. Our team of researchers come from diverse disciplines and they drive innovation, new product ideation, experimental design and testing, complex analysis and delivery of data insights around the world. We support all International Media clients and are located where our clients are.
Title: Data Scientist (ML profile) Division:  Data Science
Where will I be?We are a growing workforce integrated throughout the US and Internationally, though this particular role will be based in the United States. Working hours? We encourage working similar hours as the rest of your team to ensure overlap needed for collaboration, though are flexible based on individual needs. Can I work remotely? Of course! While many associates are located near Nielsen offices in New York, Chicago and Tampa, we leverage collaboration tools like Google Meet and Slack that enable us to work remotely. 
What is the role?We are looking to add a Data Scientist to our team of innovative Panel and Meter researchers. Our areas of expertise include researching hardware, software, quality controls and business rule changes that will improve the accuracy of our data reporting. You will learn and become an expert in Nielsen’s TV panel data, with a focus on Meter hardware and software used for data collection and crediting. You will design small and medium sized research projects, including recommendations for initial research methods (e.g. anomaly detection, classification, clustering, etc) based on project goals. You will then execute based on the defined plans and use the results of your research to recommend enhancements for production methodologies. You will also have opportunities to present research to key stakeholders and collect/implement feedback as necessary, and partner with cross-functional stakeholders to deploy their recommended methodologies into production systems. Excited? Come join us!

What will I do?

• Become an expert in TV Audience Measurement, with a focus on Meter hardware and software used for data collection and crediting. Assess project goals and provide recommended research methods (e.g. anomaly detection, classification, clustering, etc) best suited to meet project needs.
• Research and recommend enhancements to optimize data quality controls related to meter performance. Work with cross-functional teams to assess impact and implement the enhancements into Production.
• Create and manage small-to-medium sized data science projects from beginning to end based on abstract questions and concepts. Provide support for large scale projects. Support development, deployment and maintenance of data pipelines and models in a production environment
• Identify, clean, analyze and summarize data, potentially from disparate and complex data sources, to enable high quality analyses. Collaborate with stakeholders in various departments (Engineering, Data Science, Technology, etc.). This includes regularly providing status updates, developing timelines, sharing data, executing research, presenting results to an audience of various backgrounds, etc.
• Incorporate quality checks to proactively detect and correct for errors throughout analysis. Develop and share code with others, including participation in code reviews. Ensure clear documentation of research plans, results, conclusions and best practices.

Is this for me?

• You have a strong background in statistics and machine learning, theory, and practice
• You welcome new challenges and are comfortable diving into the unknown with the goal of making sense of what’s noise and what isn’t.
• You bring innovative ideas to the table and are comfortable challenging existing methods and processes. 
• You have 3+ years of experience with the following:
• Building from scratch and/or improving existing machine learning models and other related methodologies such as change point detection
• Wrangling, interpreting and manipulating complex data using tools such as Python, Spark, and SQL
• Working with cloud-based infrastructures like AWS or GCP and data pipeline automation tools such as Airflow
• Project management including working collaboratively across multiple teams
• You are comfortable defending assumptions behind ML concepts to team members and stakeholders
• Aptitude for leadership or mentoring