Tennis Data Scientist

About the Role

The role is integral to Swish Analytics' mission to revolutionize predictive sports analytics, emphasizing the use of data-driven insights to inform decision-making. By joining this team, the Tennis Data Scientist will become part of a collaborative, forward-thinking culture that encourages innovation and continuous learning. The position's remote nature allows for flexibility and work-life balance, but also requires discipline, strong time management skills, and the ability to communicate effectively in a distributed team setup.

Given the company's commitment to technical excellence and its belief in the power of engineering and mathematics in oddsmaking, the Tennis Data Scientist will have the opportunity to work with advanced technologies and methodologies. This includes developing contextualized feature sets that leverage sports-specific domain knowledge, contributing to all stages of model development, and adhering to the highest standards of software engineering.

What You Will Do

• Ideate, develop, and continually improve machine learning and statistical models to enhance the predictive capabilities of Swish Analytics' sports betting products.
• Develop and refine contextualized feature sets that incorporate deep understanding of tennis and its betting markets.
• Collaborate with data engineering and product teams to deploy new models and integrate them seamlessly into existing product offerings.
• Conduct rigorous offline and online experimentation to evaluate model performance, identify areas for improvement, and inform model development efforts.
• Analyze results and model outputs to assess performance, pinpoint weaknesses, and direct future development work.
• Engage in ongoing learning and professional development to stay abreast of the latest advancements in machine learning, statistical modeling, and data science.
• Adhere to and promote best practices in software engineering, contributing to shared code repositories and participating in code reviews.
• Document modeling work and insights, presenting findings to both technical and non-technical stakeholders.
• Collaborate with cross-functional teams to align data science efforts with broader business objectives and product strategies.
• Participate in the development of the data science team's roadmap and contribute to the growth of the department.

What We Are Looking For

• A Master's degree in Data Analytics, Data Science, Computer Science, or a related technical field.
• Demonstrated experience (2+ years) in developing models at production scale for tennis, sports betting, or a closely related domain.
• Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods.
• 5+ years of experience in developing and delivering effective machine learning and statistical models to serve business needs, preferably in sports or sports betting.
• Proficiency in relational SQL and Python, with experience in data manipulation, analysis, and visualization.
• Familiarity with source control tools such as GitHub and experience with CI/CD processes.
• Experience working in AWS environments or similar cloud-based infrastructures.
• A proven track record of strong leadership skills, with the ability to partner with teams to solve complex problems and identify innovative solutions.
• Excellent communication skills to effectively engage with both technical and non-technical audiences.

Nice to Have

• Experience with NoSQL databases and big data technologies.
• Knowledge of additional programming languages, such as R or Julia.
• Familiarity with agile development methodologies and version control systems.
• Participation in data science competitions or personal projects that demonstrate expertise in machine learning and data modeling.

Benefits and Perks

• Competitive base salary within the range of
  35,000 to

  90,000.
• Opportunities for professional growth and development in a rapidly evolving field.
• Flexible, remote work arrangements that prioritize work-life balance.
• Access to cutting-edge technologies and tools to support professional development.
• Comprehensive health insurance and wellness programs.
• Generous paid time off policy, including vacation days, sick leave, and holidays.
• Opportunities to contribute to open-source projects and engage with the broader data science community.
• Recognition and reward programs for outstanding performance and contributions to the company's mission.

• Tip: Highlight specific examples of machine learning models you've developed and deployed in production environments, especially those related to sports or betting.
• Tailor your resume and cover letter to emphasize your experience with statistical modeling, data analysis, and software engineering practices.
• Prepare to discuss your approach to model development, including how you handle data preprocessing, feature engineering, and model evaluation.
• Show proficiency in Python and SQL by providing examples of complex data analysis tasks you've performed or models you've built.
• Demonstrate your ability to communicate technical concepts to non-technical stakeholders through clear, concise explanations and visualizations.
• Be ready to discuss your experience with cloud-based services like AWS and your understanding of agile development methodologies.
• During interviews, ask questions about the company culture, the data science team's challenges, and opportunities for growth and professional development.
• Consider creating a portfolio or blog showcasing your data science projects and achievements to stand out as a candidate.