Senior Manager - Data Science

What You'll Need (Must-Have Skills)

• Educational Background: A Bachelor’s or Master’s in a quantitative field like Computer Science, Statistics, Operations Research, Economics, or a related discipline.
• Experience: 10+ years of hands-on experience in Data Science, Applied Science or a Machine Learning-focused role. 2+ years of experience in a formal leadership capacity, managing and mentoring data scientists.
• 10+ years of hands-on experience in Data Science, Applied Science or a Machine Learning-focused role.
• 2+ years of experience in a formal leadership capacity, managing and mentoring data scientists.
• Technical Proficiency: 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).
• 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).
• Core Competencies: 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.
• 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.

Why Join Swiggy?

Preferred Qualifications

• 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.
• If you are a first-principles thinker who is passionate about solving hard problems in logistics using a blend of optimization and machine learning, we want to talk to you.
• We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, disability status, or any other characteristic protected by the law.