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Mid-level Data Scientist title, common skillset, and metro locations increase applicant competition.
ML, Python, and NLP skills are broadly transferable across industries.
Explicit 4–8 years and many mandatory ML/MLOps and deep learning skills increase screening strictness.
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Design, develop, and deploy scalable machine learning and AI models for predictive analytics, risk modeling, customer segmentation, forecasting, and optimization.
Work with both structured and unstructured data to extract insights and implement AI-driven solutions integrating models into production via APIs or MLOps pipelines.
Drive advanced analytics and AI innovation across business units to enable data-driven decision-making and support agentic AI workflows.
4–8 years of professional experience in data science or related roles.
Proficiency in Python or R, SQL, and expertise in Scikit-learn and TensorFlow or PyTorch.
Knowledge of end-to-end model development lifecycle including feature engineering, hyperparameter tuning, evaluation metrics, and experience with NLP and deep learning models.
Work locations explicitly stated: Bangalore or Delhi (hybrid working mode).
Experienced in applying machine learning and AI techniques to real-world business problems with measurable impact.
Comfortable developing and deploying models in production environments using MLOps tools and handling diverse data types including text and images.
Familiarity with insurance domain and emerging AI areas such as generative AI and agentic AI workflows is advantageous.