Data Scientist

About the Role & Team
We are looking for a Data Scientist to join our Data team. The Data Science team here at Penn is responsible for building models and APIs to help improve the Barstool Sportsbook and iCasino products.  Our team values creativity, collaboration, ingenuity, and ownership.  We are looking for someone who is interested in advancing projects already underway including recommendation engines, player cohorting and classification, and A/B feature testing as well as who wants to bring ideas to the table to further our team’s mission of  creating a world-class, data-driven product experience.

The Data Science & Machine Learning team will accelerate culture transformation by understanding the business goals, identifying the fundamental business problems, delivering data solutions and integrating products that will influence overall business strategy with a focus on prescriptive analytics and calculating the return on investment. While focusing on improving players’ experiences across all Penn Entertainment touchpoints, the team develops, builds, and deploys end-to-end systems and tools at scale to unlock additional value and create competitive advantages in our products and services. The team partners with all business units across the Penn Entertainment enterprise, including Barstool Sportsbook & Casino, theScore Bet, theScore Media, our corporate partners with retail objectives, Penn Gaming Studios, Absolute, and other various parties.

About the Work
What you’ll be working on:

• User classification and player clustering: we want to better understand our players and their behaviors so that they can be responsibly engaged
• Recommendation engines: we want to direct users to content they want to see
• AB Testing: we want data to inform the development of our products and features
• Predictive & Prescriptive Analytics: we want to share in-depth knowledge on how our features are working and how our players are engaged
• Developing pipelines and developing models: we work closely with data and infrastructure engineers to deploy our models and develop required data pipelines to automate training

Key Responsibilities:

• Develop best practices for our internal data processes that include model building, insight development, and education of consumers to encourage a data-driven culture
• Design and build new predictive models and optimization routines that have an enterprise level impact. Applications include modeling and capitalizing on user behavior trends, delivering unique recommendations, and optimizing internal processes
• Collaborate with other members of the Data Science and Engineering teams on ways to approach problems, augment code, and share new techniques
• Deploy modeling deliverables in conjunction with functional team leaders and stakeholders (in Product, Operations, Marketing, etc.)
• Analyze results using solid statistical methods to inform business decisions
• Communicate clearly, efficiently, and empathetically with technical and non-technical stakeholders
• Write and maintain technical design and git/confluence documentation

Some examples of of projects:

• Customer Lifetime Value & Marketing Mix Modeling
• Reinvestment & bonus allocation
• User behavior identification and clustering
• Personalization with recommendations
• Product & feature engagement
• Forecasting primary business KPIs

About You

• 2+ years professional experience as a Data Scientist
• University degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or related field
• Proficient at writing code in Python, SQL, and using tools like Pandas to create meaningful data insights and analysis, ideally has built REST APIs with Python using FastAPI, Django, Flask, or others
• Experienced at creating algorithms, features, and applying machine learning data models to solve real business problems, creating a competitive market advantage
• Experience with API integration to automate business processes, enhancing the sharing and embedding of data between various applications and systems
• Iteratively ship DS product versions in an experimental fashion to rapidly adapt and improve products
• Understanding of classification, regression and forecasting models and A/B testing.
• Experience in cloud infrastructure, MLOps and productionizing ML models and monitoring them
• Experience in schema design and dimensional data modeling
• Experience provisioning services using Docker, Kubernetes with Terraform, Cloudwatch, GitHub, etc.
• Familiarity with Tensorflow, PyTorch, Caffe, and/or Keras
• Familiarity with experimentation and machine learning techniques

Nice to Have

• Familiarity with MLFlow
• Familiarity with Airflow
• Experience setting up ML CI / CD pipelines, testing and validating code and components, testing and validating data, data schemas, and models.
• Experience with any of Tableau, Mode, Datadog, Looker, ThoughtSpot, etc. 

What We Offer:

• Competitive compensation package.
• Comprehensive Benefits package.
• Fun, relaxed work environment.
• Education and conference reimbursements.
  
  
  
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