Senior Manager - Data Science
Swiggy · Bangalore · 10–12 yrs experience · Posted 2026-03-13
Tech stack: Python, SQL, Machine Learning, Deep Learning, Generative AI, TensorFlow, PyTorch, Keras, Pandas, NumPy, Scikit-learn, Spark, Hive, Presto, Kubeflow, MLflow, Recommendation Systems
About the role
Official Swiggy role: Senior Manager - Data Science.
Official business unit: Technology.
Location: Bangalore, India.
The Swiggy Data Science team is a vibrant group of scientists, engineers, and problem-solvers who are passionate about tackling some of the most complex challenges in machine learning at scale. We work on everything from ETA prediction and delivery partner assignment to personalized recommendations, demand forecasting, and fraud detection. We are looking for an experienced and visionary Data Science Senior Manager to lead a team of talented data scientists. In this role, you will be a player-coach, blending deep technical expertise with strategic thinking and people leadership. You will own the roadmap for a critical business area, partner with product and engineering leaders, and guide your team to build and deploy ML models that drive direct business impact. This isn't just a management role; it's an opportunity to shape the future of convenience in India.
Lead & Mentor: Lead, manage, and mentor a team of 8-10 data scientists and machine learning engineers, fostering their career growth and building a culture of innovation, accountability, and excellence.
Strategic Roadmap & Vision: Partner with Product, Engineering, and Business leaders to identify high-impact opportunities. You will translate ambiguous business problems into a clear, data-driven strategic roadmap for your team.
Responsibilities:
- Lead & Mentor: Lead, manage, and mentor a team of 8-10 data scientists and machine learning engineers, fostering their career growth and building a culture of innovation, accountability, and excellence.
- Strategic Roadmap & Vision: Partner with Product, Engineering, and Business leaders to identify high-impact opportunities. You will translate ambiguous business problems into a clear, data-driven strategic roadmap for your team.
- Technical Leadership & Execution: Oversee the end-to-end machine learning lifecycle-from ideation, data exploration, and model prototyping to deployment, monitoring, and iteration. You will ensure the team adheres to the highest standards of statistical rigor and engineering best practices.
- Problem Solving on latest Generative AI solutions: Guide your team in solving complex problems in personalization support discovery and internal productivity
- Logistics & Fulfillment: Optimizing the three-way marketplace (ETA prediction, batching, driver assignment, route optimization).
- Discovery & Personalization: Building recommendation engines, personalized search, and discovery journeys for Food, Instamart, and Dineout.
- Growth & Economics: Customer segmentation, LTV prediction, churn modeling, and designing optimal pricing and promotion strategies.
- Forecasting & Supply Chain: Demand forecasting for Instamart, restaurant supply prediction, and inventory management.
- Stakeholder Management & Communication: Effectively communicate complex technical concepts and the business impact of your team's work to a diverse audience, including senior leadership.
- Hiring & Team Building: Be a bar-raiser for talent. Actively participate in the hiring process to attract and retain top-tier data scientists.
Qualifications:
- Educational Background: A Bachelor's or Master's in a quantitative field like Computer Science, Statistics, Operations Research, Economics, or a related discipline.
- 10+ years of hands-on experience in data science or a machine learning-focused role.
- 2+ years of experience in a formal leadership capacity, managing and mentoring data scientists.
- Expert-level proficiency in Python and its data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
- Deep, hands-on experience with modern machine learning frameworks like TensorFlow, PyTorch, or Keras.
- Strong expertise in SQL and experience with large-scale data processing using tools like Spark, Hive, or Presto.
- A solid understanding of both classical ML and deep learning along with exposure to Generative AI solutions (e.g., CNNs, RNNs, Transformers).
- Problem Formulation: A proven track record of converting vague business questions into well-defined machine learning problems.
- Statistical Rigor: Deep understanding of statistical methods, experimental design (A/B testing), and causal inference.
- ML Systems Design: Experience in deploying, monitoring, and maintaining machine learning models in production environments. Familiarity with MLOps principles and tools (e.g., Kubeflow, MLflow) is a strong plus.
- Leadership: Exceptional leadership skills with a passion for developing people and building high-performing teams.
- Communication: Outstanding communication and presentation skills, with the ability to influence and align cross-functional partners.
- Experience working with real-time, low-latency model deployment and MLOps.
- Strong experience with geospatial data and relevant libraries (e.g., GeoPandas, H3, S2).
- A portfolio of patents or publications in top-tier ML/AI conferences (e.g., NeurIPS, ICML, KDD, ICLR).
- Prior experience leading and scaling a team of scientists.
Qualifications
- Educational Background: A Bachelor's or Master's in a quantitative field like Computer Science, Statistics, Operations Research, Economics, or a related discipline.
- 10+ years of hands-on experience in data science or a machine learning-focused role.
- 2+ years of experience in a formal leadership capacity, managing and mentoring data scientists.
- Expert-level proficiency in Python and its data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
- Deep, hands-on experience with modern machine learning frameworks like TensorFlow, PyTorch, or Keras.
- Strong expertise in SQL and experience with large-scale data processing using tools like Spark, Hive, or Presto.
- A solid understanding of both classical ML and deep learning along with exposure to Generative AI solutions (e.g., CNNs, RNNs, Transformers).
- Problem Formulation: A proven track record of converting vague business questions into well-defined machine learning problems.
- Statistical Rigor: Deep understanding of statistical methods, experimental design (A/B testing), and causal inference.
- ML Systems Design:
- Experience in deploying, monitoring, and maintaining machine learning models in production environments.
- Familiarity with MLOps principles and tools (e.g., Kubeflow, MLflow) is a strong plus.
- Leadership: Exceptional leadership skills with a passion for developing people and building high-performing teams.
- Communication: Outstanding communication and presentation skills, with the ability to influence and align cross-functional partners.
- Experience working with real-time, low-latency model deployment and MLOps.
- Strong experience with geospatial data and relevant libraries (e.g., GeoPandas, H3, S2).
- A portfolio of patents or publications in top-tier ML/AI conferences (e.g., NeurIPS, ICML, KDD, ICLR).
- Prior experience leading and scaling a team of scientists.
Responsibilities
- Lead & Mentor:
- Lead, manage, and mentor a team of 8-10 data scientists and machine learning engineers, fostering their career growth and building a culture of innovation, accountability, and excellence.
- Strategic Roadmap & Vision:
- Partner with Product, Engineering, and Business leaders to identify high-impact opportunities.
- You will translate ambiguous business problems into a clear, data-driven strategic roadmap for your team.
- Technical Leadership & Execution: Oversee the end-to-end machine learning lifecycle-from ideation, data exploration, and model prototyping to deployment, monitoring, and iteration.
- You will ensure the team adheres to the highest standards of statistical rigor and engineering best practices.
- Problem Solving on latest Generative AI solutions: Guide your team in solving complex problems in personalization support discovery and internal productivity
- Logistics & Fulfillment: Optimizing the three-way marketplace (ETA prediction, batching, driver assignment, route optimization).
- Discovery & Personalization:
- Building recommendation engines, personalized search, and discovery journeys for Food, Instamart, and Dineout.
- Growth & Economics: Customer segmentation, LTV prediction, churn modeling, and designing optimal pricing and promotion strategies.
- Forecasting & Supply Chain: Demand forecasting for Instamart, restaurant supply prediction, and inventory management.
- Stakeholder Management & Communication: Effectively communicate complex technical concepts and the business impact of your team's work to a diverse audience, including senior leadership.
- Hiring & Team Building: Be a bar-raiser for talent.
- Actively participate in the hiring process to attract and retain top-tier data scientists.