Job Summary: With a mission to accelerate user acquisition through data-driven intelligence, as a Senior 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, and Marketing teams to power personalized user journeys, scale experiments, and shape acquisition, retention strategy 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

About the team: Subscriptions and Payments Team powers the end-to-end direct monetization engine for India’s largest subscription platform, with over 250M+ subscriptions. We build scalable, multi-tenant systems for acquiring users via D2C and partner channels, reducing churn through intelligent renewal and retry mechanisms, and integrating deeply with global and local payment ecosystems like UPI Autopay and card mandates. Our platform drives seamless onboarding, high retention, and reliable payments at scale—enabling frictionless subscription experiences across geographies and devices.

Key responsibilities:

Analyze large datasets from various sources to identify trends, patterns, and actionable insights that help define and execute data-driven strategies for the Subscription platform
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 and the Ads platform
Present findings, methodologies, and business impact of data science initiatives to technical and non-technical stakeholders

Skills and attributes for success:

Proven track record in applied data science and machine learning, ideally in subscription, payments, or consumer-facing industries with direct revenue impact
Deep expertise in building, tuning, and evaluating supervised models — including churn prediction, propensity scoring, offer targeting, and lifetime value modeling — with demonstrable lift in production
Expert-level proficiency in Python and SQL; fluency with the modern ML stack (Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, etc.)
Strong command of statistical foundations: causal inference, experimental design (A/B and multi-armed bandits), Bayesian reasoning, and predictive modeling techniques
Extensive experience taking ML models end-to-end — from prototyping through deployment, monitoring, and iteration in production systems
Hands-on experience with big data technologies (Spark, Hadoop) and cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, or equivalents)
Familiarity with MLOps practices: feature stores, model registries, CI/CD pipelines for ML, and drift monitoring
Strong working knowledge of large language model internals — attention mechanisms, tokenization, fine-tuning paradigms and inference optimization
Hands-on experience with LLM application patterns: RAG pipelines, prompt engineering, function calling/tool use, and agent orchestration frameworks (LangChain, LlamaIndex, or similar)
Ability to evaluate LLM outputs — building eval frameworks, benchmarking model versions, and managing hallucination/reliability tradeoffs in production
Experience integrating LLMs into product workflows — e.g., intelligent customer support, personalization engines, or automated insights generation
Strong product intuition to translate business objectives (reduce churn, boost conversion, improve retention) into scoped, high-impact data science initiatives with measurable outcomes
Ability to define success metrics, size opportunities, and align stakeholders before a line of model code is written
Demonstrated ability to mentor and raise the bar for junior and mid-level data scientistsExcellent communicator — can distill complex model behavior, tradeoffs, and results for both technical peers and non-technical leadership
Comfortable driving ambiguous, cross-functional problems from problem definition through delivery

Preferred education and experience:
Master's or PhD in Computer Science, Statistics, Mathematics, or related quantitative field with 9+ years of hands-on experience in applied machine learning or data science

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.