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Junior Data Scientist

6 months ago

Toronto, Canada Score Media and Gaming Inc. Full time

theScore, a wholly-owned subsidiary of PENN Entertainment, empowers millions of sports fans through its digital media and sports betting products. Its media app 'theScore' is one of the most popular in North America, delivering fans highly personalized live scores, news, stats, and betting information from their favorite teams, leagues, and players. theScore's sports betting app 'theScore Bet Sportsbook & Casino' delivers an immersive and holistic mobile sports betting and iCasino experience. theScore Bet is currently live in the Company's home province of Ontario. theScore also creates and distributes innovative digital content through its web, social and esports platforms.

**About the Role & Team**

We are looking for a Junior-Level 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 ESPN Bet and theScore Bet 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.

**Things our team will be working on**
- Recommendation engines: Data scientists can build recommendation engines to suggest bets to users based on their past betting history and other factors, such as their favorite teams and sports. This can help users to find bets that they are more likely to win and to increase the overall betting experience.
- Player clustering and classification: Data scientists can use clustering and classification algorithms to group players into different categories based on their betting behavior. This information can be used to tailor marketing and promotional campaigns to specific groups of players and to identify potential problem gamblers.
- A/B feature testing: Data scientists can use A/B feature testing to test different versions of the sportsbook website and app to see which ones perform better. This information can be used to improve the user experience and to increase the number of bets that are placed.
- Document classification and extraction: Data scientists can use natural language processing (NLP) techniques to classify and extract information from documents, such as news articles and social media posts. This information can be used to identify trends in the betting market, to identify potential fraud, and to generate insights for the sportsbook's traders.
- Sentiment analysis: Data scientists can use sentiment analysis to identify the sentiment of users on social media and other online platforms. This information can be used to understand public opinion about the sportsbook and to identify potential problems.

**Responsibilities**
- **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

**Requirements**:

- 0-2 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
- 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.
- Familiarity with cloud infrastructure, MLOps and productionizing ML models and monitoring them
- Familiarity with schema design and dimensional data modeling
- Familiarity with using Docker, Kubernetes with Terraform, Cloudwatch, GitHub, etc.
- Familiarity with Tensorflow, PyTorch, Caffe, and/or Keras
- Familiarity with experimentation and machine learning techniques

**What We Offer**
- Competitive compensation package
- Fun, relaxed work environment
- Education and conference reimbursements.
- Parental leave top up
- Opportunities for career progression and mentoring others
- Opportunities for career progression and mentoring others

LI-REMOTE
- theScore is committed to creating a di