Data Science Manager, Football

1 Month ago • All levels • Data Analysis • $150,000 PA - $200,000 PA

Job Summary

Job Description

PENN Entertainment is seeking a Data Science Manager to lead a team responsible for building models, forecasts, and simulations for their sports betting products, specifically ESPN BET and theScore Bet. The role involves identifying and prioritizing data science projects, managing and mentoring a team of data scientists, and owning project roadmaps, workflows, and deliverables. The manager will act as a subject matter expert in football (NFL/NCAAFB) and the sports betting industry, focusing on machine learning, statistical modeling, and predictive/prescriptive analytics. Key responsibilities include monitoring production model stability, collaborating with stakeholders and executive leaders, developing best practices for data processes, and designing predictive models with enterprise-level impact. The position requires strong leadership, communication, and critical thinking skills to drive actionable insights and business value.
Must have:
  • Lead a team of data scientists.
  • Expertise in NFL/NCAAFB data.
  • Sports betting industry knowledge.
  • Machine learning and statistical modeling.
  • Strong communication and leadership skills.
  • Experience scaling data products.
  • Proficiency with SQL, Pandas, Numpy, SKLearn.
Good to have:
  • Familiarity with MLFlow.
  • Experience with ML CI/CD pipelines.
  • Experience with Tensorflow, PyTorch, Caffe, Keras.
  • Experience in sports beyond NFL/NCAAFB.
Perks:
  • Competitive compensation package
  • Education and conference reimbursements
  • Parental leave
  • Opportunities for career progression and mentoring others

Job Details

PENN Entertainment, Inc. is North America’s leading provider of integrated entertainment, sports content, and casino gaming experiences. From casinos and racetracks to online gaming, sports betting and entertainment content, we deliver the experiences people want, how and where they want them.

We’re always on the lookout for those who are passionate about creating and delivering cutting-edge online gaming and sports media products. Whether it’s through ESPN BET, Hollywood Casino, theScore Bet Sportsbook & Casino, or theScore media app, we’re excited to push the boundaries of what’s possible. These state-of-the-art platforms are powered by proprietary in-house technology, a key component of PENN’s omnichannel gaming and entertainment strategy.

When you join PENN Entertainment’s digital team, you’ll not only work on these cutting-edge platforms through theScore and PENN Interactive, but you’ll also be part of a company that truly cares about your career growth. We’re committed to supporting you as you expand your skills and explore new opportunities.

With locations throughout North America, you can build a future at PENN Entertainment wherever you are. If you want to challenge conventions in gaming, media and entertainment, we want to talk to you.

About the Role & Team

We are looking for a Data Science Manager to lead a team of data scientists The Data Science team here at Penn is responsible for building models, forecasts, and simulations to help improve theScore Bet and ESPN Bet products. Our team values creativity, collaboration, ingenuity, and ownership. We are looking for someone who is interested in joining a team building and enhancing an industry leading football (NFL / NCAAFB) model for theScore Bet and ESPNBet football product. This position is a rare opportunity enabling a highly motivated candidate to make a significant impact on an enterprise grade football data product.

About the Work

  • Help identify and prioritize impactful data science projects as well define goals & objectives for the team.
  • Manage and mentor team members as they progress through their career progression journey.
  • Own decisions for Data Science project prioritization, roadmap, workflows, tooling, integrations, modeling techniques, algorithm, deliverables, etc.
  • Subject Matter Expertise: Be the football subject matter expert for your teamto ensure data-driven solutions are in-line with sport-specific best practices
  • Betting Expertise: Knowledge of the sports betting industry and demonstrated experience using football data to solve pricing problems
  • Machine learning & statistical modeling: we want to be able to predict the likelihood or expected outcome of various events across different sports.
  • Predictive & Prescriptive Analytics: we want to share in-depth knowledge on how our features are working and our forecasts are performing.
  • Own relationships & touchpoints with primary stakeholders for each project in flight / on the roadmap & in production.
  • Responsible for monitoring stability and accuracy of data science projects in production.
  • Interact regularly department leaders; proactively initiate ideas to improve services or products; contribute to department or group objectives, strategy and priorities.
  • Partner cross functionally with key executive leaders within the organization to leverage data in new and innovative ways to drive actionable insights and business value.
  • Develop best practices for our internal data processes that include model building, modeling techniques, improved latency and readability.
  • Design and build new predictive models and optimization routines that have an enterprise level impact. This varies from modeling expected sporting outcomes at an event level to utilizing various game state data to simulate full spectrums of expected outcomes.
  • Write and maintain technical design and git/confluence documentation.
  • Other duties as required.

About You

  • Expert-level skills in solving quantitative problems with NFL and/or NCAAFB data & using sports data to solve pricing problems.
  • A passion for sports and sports betting
  • A Strong grasp of sports betting terminology, the sports betting industry, and betting specific data sources
  • A deep understanding of utilizing sport-specific problem-solving techniques to model various elements of the game.
  • A deep understanding of utilizing player and ball tracking data and enriched NFL and/or NCAAFB play-by-play data to solve football problems and how to utilize model predictions to augment various forecasts.
  • Knowledge of best practices in dynamically projecting various aspects of player and team performance.
  • Strong grasp on the fundamentals of simulation modeling and latency reduction techniques.
  • Previous experience leading a team either in football or in the sports betting industry
  • Previous experience scaling an NFL and/or NCAAFB data product that provided demonstrable value
  • Excellent written, communication, organization, and presentation skills.
  • The ability to lead, mentor, and efficiently run a team of data scientists.
  • Excellent critical thinking skills to enhance problem-solving techniques, ensure data products are incredibly resilient, and the ability to identify potential edge-cases
  • Experience with being provided a high-level long-term objective and leading a team of data scientists to provide a solution.
  • Experience working cross-functionally with engineering teams and provisioning services using GitHub.
  • Strong experience with traditional DS tools and frameworks such as SQL, Pandas, Numpy, SKLearn etc.
  • Experience with any of Docker, Kubernetes with Terraform, Cloudwatch.
  • Experience with DAG orchestration tools suchs as Argo Workflows, Step Functions, Cloud Fucntions, Dagster, Airflow, etc.
  • Significant experience with AWS/GCP.

Nice to Have

  • Familiarity with MLFlow.
  • Experience setting up ML CI/CD pipelines, testing and validating code/data, managing databases, and deploying models.
  • Experience with SKLearn, Tensorflow, PyTorch, Caffe, and/or Keras.
  • Previous experience working in sports other than the NFL or NCAAFB

What We Offer

  • Competitive compensation package
  • Education and conference reimbursements
  • Parental leave
  • Opportunities for career progression and mentoring others

#LI-REMOTE

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Toronto, Ontario, Canada (Remote)

Philadelphia, Pennsylvania, United States (Remote)

Toronto, Ontario, Canada (Remote)

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Toronto, Ontario, Canada (Remote)

Philadelphia, Pennsylvania, United States (Remote)

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