Project-Sports Data Scientist

Key Skills

Python Programming, Data Analytics, R Programming

Job Description

*This is a 24 month project position*

The ESPN Sports Analytics team is a multidisciplinary group where all members have a deep knowledge of sports, statistics, databases, programming logic and languages, and storytelling. Combining best-in-the-industry data with advanced mathematics and statistical modeling skills, Sports Analytics has created storytelling tools and metrics that have improved the evaluation of team and player performance, such as Total QBR and power ratings for NFL, NBA, college football and college basketball. These products make fans better informed and help keep ESPN at the cutting edge of sports statistical analysis.

The Sports Data Scientist will be the lead architect in designing and creating a NBA player adjusted plus-minus metric for use across all platforms, including within other predictive metrics. A secondary phase will include the modeling and development of player box score stats predictions, both pregame and in-game. The Sports Data Scientist will work with the Analytics leadership team to define project timelines and delegate duties. The incumbent must demonstrate critical thinking about the execution of the projects and keep Analytics leadership informed of progress. Completing work on deadlines is a crucial requirement. A portfolio of successful analytics projects and metrics is necessary to be considered for this position. The Sports Data Scientist will have the skills to partner with other Analytics Specialists as well as Content teams and Technology and Digital Product staffers on projects. Candidates need to have a deep understanding of Bayesian statistics and a background in constructing predictive models and complex formulas that explore new areas of analytics, ideally in sports.

Ideal candidates will possess the following traits:

• Meticulous attention to detail in striving for 100 percent accuracy
• The ability to thrive under deadlines
• The ability to work both independently on a project with little supervision and as part of a team
• Strong technical skills and the ability to understand ESPN’s data structure
• Demonstrated excellence working with multiple departments to achieve a common goal
• Strong verbal and presentation skills
• Demonstrated ability to regularly produce ideas on areas of opportunity within performance analytics for ESPN

Responsibilities:

• Use advanced mathematics and statistical modeling to formulate sports performance metrics
• Identify the key components in these complex formulas and turn them into language that allows them to be readily used in storytelling across ESPN platforms
• Work collaboratively through conflicts and gain support from internal project partners and stakeholders with various interests and priorities
• Collaborate with Analytics staffers and ESPN technology partners to write scripts, queries and basic code to transform data into automated metrics across our digital platforms and internal research tools
• Use strategic analytical approaches and techniques using data to answer sports questions

Basic Qualifications:

• A minimum of 5 years’ experience with sports analytics, or any equivalent combination of education and experience that provides the applicant with the knowledge, skills and ability to perform the job
• A minimum of 5 years’ experience constructing statistical models to explain or predict performance
• A minimum of 5 years’ experience in a programming language such as R or Python
• Strong knowledge in using SQL to query and modify data from databases
• A passion for sports and statistics; candidates need to have a working knowledge of players, teams and the rules of the games
• Full availability for this position, which will include nights, weekends and holidays
• Demonstrated ability to ensure accuracy of content
• Effective communication and leadership skills
• A thorough knowledge of sports statistical analysis in the marketplace
• Strong sports knowledge, both historical and current

Preferred Qualifications:

• Expert knowledge in at least one statistically relevant programming language, such as R or Python.
• Demonstrated portfolio of predictive modeling experience in sports, particularly previous experience with an adjusted plus-minus metric.
• Deep understanding of the NBA and the current state of data therein.
• Previously worked with and have a solid understanding of NBA player tracking data.
• Strong understanding of sports gambling and fantasy, particularly around player performance. Experience with machine learning or other artificial intelligence techniques
• Experience turning sports analytics into storylines
• Experience writing about sports analytics

Required Education

• College degree in statistics, engineering, mathematics, econometrics or a related field

Preferred Education

• Advanced college degree in statistics, engineering, mathematics, econometrics or a related field