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Tier-1 brand, metro Bangalore, mid-level generalist Data Scientist title, and broad skill requirements amplify applicant competition.
Applied ML, Python and SQL skills are transferable, but enterprise data platform experience increases sector specificity.
Explicit 3-5 years plus mandatory Python, SQL, ML libraries and model deployment experience enforce moderate filtering.
Develop and deploy machine learning models for forecasting, classification, recommendation, optimization, clustering, and anomaly detection in enterprise-scale data platforms.
Collaborate with consultants, client stakeholders, and cross-functional teams to translate business problems into analytical tasks and communicate model insights effectively.
Support end-to-end model lifecycle including data preparation, feature engineering, training, validation, deployment support, monitoring, and continuous improvement using platforms like Snowflake, Databricks, and Microsoft Fabric.
3-5 years of commercial experience in Data Science, Machine Learning, or Advanced Analytics roles.
Strong hands-on experience in Python (including Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow, or PyTorch) and SQL for data analysis and model development.
Solid theoretical and practical knowledge of supervised and unsupervised learning, statistical modeling, feature engineering, validation, model evaluation, and optimization.
Work Experience Required: 3-5 years; Location: Bangalore, Karnataka, India; Notice Period: Not explicitly mentioned in the JD.
Experienced in moving machine learning models beyond exploratory analysis to documented, testable, and reusable assets with exposure to pilot or production environments.
Familiar with MLOps practices including model versioning, reproducibility, deployment handover, and monitoring for model performance.
Comfortable working collaboratively in mixed technical and business teams and skilled at communicating model assumptions, limitations, and business impact, with awareness of responsible AI, bias, and data quality considerations.