Lead Data Scientist - Data Engineer

5 - 7 years experience  • 

Salary depends on experience
Posted on 03/21/18
Chicago, IL
5 - 7 years experience
Salary depends on experience
Posted on 03/21/18

The Lead Data Scientist leads multiple implementation projects to engineer our data pipeline and design insightful analytics for Nielsen’s TV Products.

Role Responsbilities:

  • Learn and become an expert in TV Nielsen knowledge, with a focus on the data model, weighting, computations, and enhanced methodologies.

  • Support analyses for Data Science teams including custom requests, enhancement testing, client inquiries, impact analysis, standards and best practices.

  • Learn and become an expert in how big data flows through Nielsen systems in order to create reliable data views that will feed into client inquiry processes.

  • Train and empower others with relevant knowledge. Create and maintain Data Science requirement documents, and training materials.

  • Work with cross-functional teams to implement and validate enhanced audience measurement methodologies.  

  • AppDev teams to validate production implementations of complex methodology and engineer our internal data model.

  • Collaboratively create and manage projects from beginning to end; including developing analytical plan, running analyses, and summarizing results while effectively managing expectations with key stakeholders.

  • Respond to client inquiries: Examine analysis specifications for completeness, determine how to execute the analysis with available data, modify specifications as needed, provide an estimated delivery date, and accurately deliver analyses on time with a summary of the key insights.

  • Provide oversight and consultation to Data Scientists and Analysts.                     

  • Convert existing SAS or C code to Python code and perform quality tests.

  • Independently write custom Python code.

  • Detect and address quality escapes.

  • Pro-actively gather information, as needed, to work independently as well as in a team environment.

  • Key tasks include – but are not limited to – data integration, automation, examining large volumes of data, identifying trends and being able to understand the methodology to be able to explain said trends, and identifying methodological and process improvements. Other project areas may include representation/sampling, bias reduction, and indirect estimation.

Job Requirements:

Master’s degree in Data Science, Analytics, Statistics, Economics, Social Science, Operation Research, Mathematics, or Computer Science with outstanding analytical expertise. (Bachelor’s degree might be considered for candidates with exceptional and relevant experience.)

5 - 7 years of experience and demonstrated domain expertise with one or more of the following: python programming, data manipulation, data engineering, data integration, weighting, sampling, survey or market research, trend or pattern recognition, statistics, data aggregation, data fusion, process automation, or quality assurance.   

Must be proficient with Python and SQL. Excellent technical documentation skills are also expected.

Must have experience working with big data, algorithms, and large-scale databases

Must have passion for data. Ability to manipulate, analyze, interpret large data sources, and tell a story from data through analyses

Must have strong verbal and written communication skills.

Must have an aptitude for leadership or mentoring, ability to work alone, with peers, and as a project leader

Must have ability to manage multiple projects, align expectations with stakeholders, communicate timing and expectations.

Must have critical thinking skills necessary to evaluate results in order to make decisions

Must have desire to grow as an expert in Local TV Audience Measurement for a long term career.

Must demonstrate intellectual curiosity and persistence to find answers. Learn quickly and explain complex data models and methodologies to a variety of audiences.

Demonstrate interest in Nielsen methodologies, data collection, platforms, research processes and operations.

Preferred experience with data visualization tools (e.g. Tableau, Spotfire, Quicksight, Zoomdata)

Preferred proficiency in Unix or Linux environment

Preferred experience with Databricks, Spark, Scala.

Lead Data Scientist - Data Engineer - 9792

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