Swish Analytics is hiring a Tennis Data Scientist to join its remote team based in the United States. The company sits at the intersection of sports betting, fantasy sports, and predictive analytics. Landing this role means bringing serious technical depth and a genuine passion for tennis data together in one package.
Salaries range from $135,000 to $190,000 annually. That range reflects how competitive and specialized this position truly is. Swish is not looking for generalists.
What Swish Analytics Actually Does
Swish Analytics builds predictive data products for the sports betting and fantasy sports industries. The company treats oddsmaking as an engineering and mathematics problem, not a gut-feeling exercise. Every model and algorithm serves a real commercial product used by enterprise and consumer clients.
Data Science is central to Swish's business model. That means data scientists here carry real ownership over the products they build. This is not a support function sitting behind another team.
Technical Skills Required for This Role
Machine Learning and Statistical Modeling
The core of this job is building and improving machine learning models and statistical models. You need hands-on experience developing predictive systems from scratch. Swish specifically mentions producing state-of-the-art sports betting products, so your models must perform under real commercial pressure.
Familiarity with both classical statistics and modern ML approaches matters here. Regression, survival analysis, Bayesian methods, and gradient boosting all have relevant applications in tennis prediction. Knowing when to use which method separates strong candidates from weak ones.
Programming and Software Engineering
Strong Python skills are essential for this kind of role. Most sports analytics teams build their pipelines in Python, using libraries like scikit-learn, PyTorch, or TensorFlow. You also need to write clean, maintainable code that others can read and build on.
Swish explicitly requires adhering to software engineering best practices and contributing to shared code repositories. That means version control with Git, proper documentation, code reviews, and structured development workflows. Data scientists who write sloppy code do not fit this environment.
- Python proficiency with ML libraries
- SQL for querying and managing large datasets
- Git and version control workflows
- Experience with model deployment pipelines
- Familiarity with cloud platforms like AWS or GCP
Feature Engineering and Domain Knowledge
One specific requirement stands out: developing contextualized feature sets using sports-specific domain knowledge. For tennis, this is a nuanced challenge. Surface type, match format, player fatigue, head-to-head records, and serve statistics all shape outcomes in complex ways.
You need to understand tennis well enough to know which variables actually predict results. Raw data alone does not build good models. Knowing that a player's second-serve win rate on clay differs wildly from hard court performance is the kind of insight that drives meaningful features.
Experimentation and Model Evaluation
Swish expects candidates to improve model performance through rigorous offline and online experimentation. That requires designing proper A/B tests, understanding statistical significance, and interpreting results without bias. Identifying where a model fails is just as important as knowing where it succeeds.
Experience with backtesting sports prediction models is a major advantage. Betting markets move fast, and models need constant evaluation against live results. You must be comfortable diagnosing model weaknesses and translating those findings into concrete development priorities.
Soft Skills That Matter at Swish Analytics
Communication Across Technical and Non-Technical Teams
The job posting specifically asks candidates to document modeling work and present to stakeholders, including both technical and non-technical partners. That is a meaningful requirement. Many data scientists struggle to explain their models in plain language.
At Swish, your work connects to product decisions and business strategy. The ability to translate model outputs into clear narratives directly affects how your work gets used. Strong written and verbal communication skills are not optional here.
Collaboration and Team Orientation
Swish uses the phrase "team-oriented individuals" deliberately. Data scientists here partner with data engineering teams and product teams throughout the model development lifecycle. That requires patience, flexibility, and genuine willingness to work across functions.
Working in isolation is not an option. You will co-own projects with people who have very different technical backgrounds. Showing that you can collaborate effectively is part of what makes a candidate competitive for this role.
Comfort With Ambiguity
Swish describes its environment as fast-paced and continually evolving. The company openly states that its challenges are unique and that candidates should be comfortable in uncharted territory. Sports betting analytics is still a young field, and many problems do not have established solutions.
Candidates who need detailed instructions and clear-cut answers will find this environment difficult. The ability to define a problem, hypothesize solutions, and iterate quickly is what Swish is actually hiring for.
Experience and Education Requirements
A Master's degree in Data Analytics, Data Science, Computer Science, or a related technical field is required. Swish is not flexible on this point. The quantitative foundation that graduate programs provide directly applies to the modeling work this role demands.
Demonstrated experience developing models in a professional setting is equally important. Academic projects alone are unlikely to satisfy hiring managers at a company operating in live betting markets. Real-world deployment experience matters significantly.
- Master's degree in a quantitative field
- Professional experience with ML model development
- Background in sports analytics or sports betting preferred
- Experience working with time-series or event-based sports data
- Proven track record of improving model performance over time
How to Build These Skills
Strengthen Your Statistical and ML Foundation
If your graduate coursework focused heavily on theory, you need to close the gap between theory and application. Platforms like Coursera, edX, and fast.ai offer practical ML courses that translate directly to real projects. Building and shipping models on your own is the fastest way to develop professional-grade instincts.
Focus specifically on probabilistic modeling and Bayesian statistics. Tennis prediction leans heavily on probability distributions and uncertainty quantification. Those skills differentiate strong candidates in sports analytics specifically.
Develop Tennis Domain Expertise
Follow tennis analytics communities on platforms like Twitter and Substack. Researchers like Jeff Sackmann have published open tennis datasets through projects like Tennis Abstract. Downloading those datasets and building your own prediction models is one of the most direct ways to prepare for this role.
Study how surface, draw difficulty, and player momentum interact in match outcomes. Build features from raw point-by-point data. Document your findings clearly, because that documentation itself becomes a portfolio asset.
Practice Software Engineering Discipline
Data scientists who want to work at product-focused companies need to write production-quality code. Start using Git religiously for every project. Write unit tests. Follow PEP 8 style guidelines. Practice structuring repositories the way engineering teams do.
Contributing to open-source sports analytics projects also builds both skills and visibility. It demonstrates that you write code other people can read and trust.
Build a Public Portfolio
Publish your tennis analytics work on GitHub or a personal blog. Write clear explanations of your methodology and results. Hiring teams at data-first companies like Swish look at what candidates have actually built, not just their resume bullet points.
A well-documented tennis prediction project, even an imperfect one, shows more than a degree alone ever could. It proves you can move from concept to execution independently.
Candidates ready to apply for the Swish Analytics Tennis Data Scientist position can find the full job listing and application at https://himalayas.app/companies/swish-analytics/jobs/tennis-data-scientist.
