Staff Data Scientist

**Job Summary**:With a mission to accelerate user acquisition through data-driven intelligence, as a Staff Data Scientist, you will design and deploy machine learning models and decision systems that drive subscription growth via direct-to-consumer (D2C) and B2B/partner channels. You’ll collaborate closely with Product, Engineering, Growth, Content and Marketing teams to power personalized user journeys, scale experiments, and shape acquisition, retention strategy, revenue attribution modeling through insights and automation. If you thrive at the intersection of data, product, and impact — and want to shape how millions of users subscribe — this is the role for you

**Key responsibilities**:

- Analyze large datasets from various sources to identify trends, patterns, and actionable insights that help define and execute data-driven strategies
- Design, develop, and deploy statistical models, machine learning algorithms, generative AI models, and predictive analytics to optimize plan recommendation, churn predictions, subscription propensity, winback strategies
- Integrate machine learning models into production systems to deliver real-time insights and automated decision-making capabilities
- Collaborate closely with cross-functional teams, including Data Engineering, Product Management, Growth and Business teams, to understand business challenges and deploy end-to-end solutions
- Continuously evaluate and improve data quality and model performance to ensure consistent, reliable, and accurate insights
- Develop and execute A/B testing frameworks and experimental designs to validate algorithmic improvements and quantify business impact
- Stay up-to-date with the latest advancements in data science, machine learning, and AI, and proactively incorporate cutting-edge techniques into models
- Present findings, methodologies, and business impact of data science initiatives to technical and non-technical stakeholders

**Skills and attributes for success**:

- Proven experience in data science and machine learning preferably in subscription modeling, revenue modeling or related industries
- Experience building and tuning supervised models (e.g., churn prediction, propensity scoring, offer targeting)
- Strong proficiency in Python, SQL, and relevant data science libraries (Pandas, Scikit-learn, TensorFlow, PyTorch, etc.)
- Expertise in building and deploying machine learning models into production systems
- Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Google Cloud)
- Deep understanding of statistical analysis, machine learning, data mining, and predictive modeling techniques
- Should be comfortable leveraging LLMs and internals
- Strong product intuition to translate business goals (e.g., reduce churn, boost conversion) into actionable data science solutions with measurable impact
- Strong problem-solving skills with the ability to translate business problems into data science solutions
- Excellent verbal and written communication skills, with the ability to present complex information to non-technical stakeholders

**Preferred education and experience**:

- Master's or PhD in Computer Science, Statistics, Mathematics, or related quantitative field with 7+ years of experience in applied data science/machine learning

**About Us**

Perched firmly at the nucleus of spellbinding content and innovative technology, JioStar is a leading global media & entertainment company that is reimagining the way audiences consume entertainment and sports. Its television network and streaming service together reach more than 750 million viewers every week, igniting the dreams and aspirations of hundreds of million people across geographies.
- JioStar is an equal opportunity employer. The company values diversity and its mission is to create a workplace where everyone can bring their authentic selves to work. The company ensures that the work environment is free from any discrimination against persons with disabilities, gender, gender identity and any other characteristics or status that is legally protected._