PENN Entertainment, Inc. [https://www.pennentertainment.com/] 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 [https://about.espnbet.com/], Hollywood Casino, theScore Bet
Sportsbook & Casino [https://thescore.bet/], or theScore
[https://www.thescore.com/] 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.
Work-term: January 5, 2026 - April 24, 2026
Number of Openings: 1
About the Role & Team
The Data Science & Machine Learning team is responsible for building models and
APIs to help improve all of Penn Entertainment's digital offerings. Our team
values creativity, collaboration, ingenuity, and ownership. As a machine
learning engineer, you will get the opportunity to contribute to, optimize, and
deploy many exciting models as well as help the team build net-new features into
our machine learning platform.
Examples of some of our on-going projects:
- Recommendation engines: we want to direct users to content they want to see.
- Experimentation frameworks: we want to understand the impact our products
have on users
- Chat-Toxicity Modelling: create an inclusive community chat environment.
- Cross-sell Likelihood: enable users to access the full range of
Penn Entertainment's offerings.
- Bot User Identification: fight fraud on Penn Entertainment’s digital
offerings by identifying non-human users
About the Work
As a key member of our Machine Learning Engineering team, you will:
- Assist in the design and development of new machine learning pipelines
- Help deploy models and deliverables in conjunction with functional team
leaders and stakeholders (in Product, Operations, Marketing, etc.)
- Improve our machine learning platform by implementing ML ops best practices.
- Conduct thorough testing and evaluation of new tools and technologies
to assess their suitability for our platform.
- Communicate clearly and efficiently with technical and
non-technical stakeholders.
- Write and maintain technical design and git/Confluence documentation.
- Other duties as required.
About You
- Currently enrolled in a university degree in Computer Science, Data Science,
Statistics, Computer Engineering, or a related technical field.
- Experience in deploying applications using Docker, Kubernetes, Terraform,
GitHub and other relevant tools.
- Proficient with Python and SQL. Languages like Go, Rust, Scala, R, and C++
are nice-to-have.
- Proven expertise in setting up Continuous Integration/Continuous
Deployment (CI/CD) pipelines for Machine Learning projects. Skilled in
testing and validating code, data, data schemas, and models.
- Demonstrated experience developing machine learning pipelines
with orchestration tools like Airflow, Kubeflow, or Dagster.
- Extensive experience building and/or contributing to dbt projects.
- Experience developing and deploying machine learning solutions in a
public cloud such as AWS, Azure, or Google Cloud Platform is preferred.
- Familiarity with popular machine learning frameworks such as TensorFlow,
PyTorch, Caffe, and/or Keras
Nice To Have
- Experience building real-time stream processing solutions with technologies
such as Kafka, Spark, and Flink.
- Experience with virtual feature store technologies such as Featureform or
Feast.
- Experience integrating with BI tools such as Mode, Tableau, Looker, or
- Background in deploying and monitoring large language models (LLMs).
What We Offer:
- Fun, relaxed work environment
- A voice. We're dedicated to open communication which empowers our employees
to drive the company's culture
- A company that encourages a culture of inclusion and diversity
- Opportunity to work on large-scale consumer-facing applications with millions
of users
Candidates residing in Ontario requiring special accommodation can email
accessibilityoffice@thescore.com
Penn Interactive is committed to creating a diverse environment and is proud to
be an equal opportunity employer. All qualified applicants will receive
consideration for employment without regard to race, color, religion, gender,
gender identity or expression, sexual orientation, national origin, genetics,
disability or age.
