Data Scientist

About the Role

As a Data Scientist at OnSportsNow, you'll be at the intersection of sports, data, and technology, developing advanced statistical models and algorithms that power our predictive features and enhance the user experience. You'll work with large datasets from various sports leagues to extract meaningful insights and create compelling data visualizations that help fans better understand the games they love.

This role is perfect for someone who is passionate about sports analytics and has a strong foundation in statistical modeling, machine learning, and data analysis. You'll collaborate with product managers, engineers, and content creators to build innovative features that differentiate OnSportsNow in the competitive sports media landscape.

Key Responsibilities

• Develop and implement statistical models for analyzing sports data, including player performance, team dynamics, and game outcomes
• Create predictive algorithms for game projections, player statistics, and other sports-related forecasts
• Design and build data pipelines to process, clean, and transform large datasets from multiple sports leagues
• Collaborate with engineers to integrate data models into production systems
• Work with the product team to identify opportunities for data-driven features and enhancements
• Develop visualizations and interactive tools to communicate complex sports analytics in an accessible way
• Research and implement new methodologies from sports analytics literature
• Monitor and evaluate model performance, making refinements as necessary
• Stay current with advancements in data science, statistics, and sports analytics
• Document methodologies, approaches, and findings to ensure knowledge sharing across the team

Qualifications

Required:

• Master's degree or higher in Statistics, Computer Science, Mathematics, or related quantitative field
• 3+ years of professional experience in data science, machine learning, or statistical modeling
• Strong programming skills in Python and experience with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch)
• Experience building and deploying machine learning models in production environments
• Proficiency with SQL and working with relational databases
• Knowledge of statistical concepts, hypothesis testing, and experimental design
• Excellent data visualization skills (Matplotlib, Seaborn, D3.js, or similar tools)
• Strong problem-solving skills and attention to detail
• Ability to communicate complex technical concepts to non-technical stakeholders
• Passion for sports and understanding of sports statistics

Preferred:

• PhD in a quantitative field with a focus on machine learning or statistical modeling
• Experience with sports analytics or working in the sports industry
• Knowledge of advanced ML techniques such as deep learning, reinforcement learning, or natural language processing
• Experience with big data technologies (Spark, Hadoop, etc.)
• Familiarity with feature engineering and selection techniques
• Background in time series analysis and forecasting
• Experience with cloud platforms (AWS, GCP, or Azure)
• Contributions to open-source projects or research publications
• Knowledge of causal inference and probabilistic modeling

What We Offer

• Competitive Compensation: Salary range of $115,000 - $155,000, depending on experience, plus equity
• Health Benefits: Comprehensive health, dental, and vision insurance for you and your family
• Time Off: Unlimited PTO policy, plus company holidays
• Professional Development: Annual learning budget for conferences, courses, and certifications
• Retirement: 401(k) with company matching
• Sports Perks: Access to sports events and game tickets
• Equipment: MacBook Pro and home office stipend
• Team Building: Regular team retreats and social events
• Wellness Programs: Mental health resources and fitness subsidies
• Research Opportunities: Support for publishing papers and attending academic conferences

Interview Process

1. Initial Screening: 30-minute call with a recruiter to discuss your background and the role
2. Technical Assessment: A take-home data science challenge using real sports data (time-boxed to 4 hours)
3. Technical Interview: 1-hour video call with senior data scientists to discuss your solution and technical background
4. Modeling & Algorithm Interview: 1-hour deep dive into statistical modeling and machine learning approaches
5. Team Fit: 1-hour conversation with the data science manager and potential teammates
6. Final Round: Brief meeting with a senior leader

We aim to complete the entire process within 2-3 weeks and provide feedback at each stage.