Data Scientist

About Sixer Sixer is an innovative online platform that enables cricket fans to experience the excitement of trading cricket players. By tracking player performance, our revolutionary fantasy game allows you to build a portfolio of players and watch as their value fluctuates based on their on-field success. Sixer is transforming fan engagement and redefining how cricket is enjoyed with its interactive and thrilling ecosystem.
Join Sixer today and experience the thrill of a fantasy sports market like never before!For more information: https://sixergame.com/

Role OverviewWe are looking for a Data Scientist specializing in product and customer analytics. The ideal candidate has a strong foundation in machine learning and statistics, with experience applying these skills to solve business problems such as churn prediction, customer lifetime value (LTV) modeling, bonusing optimization, and customer journey analysis. While your primary focus will be product analytics, you should be comfortable stepping into other data science or analytics tasks, e.g. sports analytics or dashboarding. Key Responsibilities

• Data Analysis & Insights: Analyze user behaviour and product interactions to uncover trends and actionable insights. Collaborate with Marketing, Product, and Operations teams to design experiments and interpret results.
• Predictive Modeling: Develop and optimize models for churn prediction, LTV, customer segmentation, and recommendations. Define success metrics and performance benchmarks with stakeholders.
• Reporting: Provide quick, data-driven reports to support strategic and operational decisions.
• Model Development & Deployment: Train, validate, and deploy models using AWS SageMaker or similar platforms. Ensure seamless integration and continuous performance monitoring.
• Communication: Present insights in a clear, impactful manner for both technical and non-technical audiences. Work with data engineers to enhance pipelines and maintain data quality.
• Cross-Domain Support: Apply analytical expertise to sports analytics or other evolving projects as needed.

Qualifications

• 2–3 years in a data science or machine learning role, preferably with exposure to customer analytics.
• Data Stack: Python, Jupyter Notebook, Redshift, AWS SageMaker, Metabase
• Solid understanding of supervised/unsupervised learning, feature engineering, and model evaluation.
• Proficient in statistical analysis, hypothesis testing, and experiment design.
• Comfortable with SQL and data preparation/querying.
• Understanding MLOps and model deployment.
• Strong ability to translate complex findings into actionable insights for stakeholders.
• Interest or experience in finance and/or sports analytics.
• Bonus: Knowledge of econometrics.