AI/ML Data Scientist

AI/ML Data Scientists

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  Department: Data Science & Advanced Analytics

  
  


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  Experience Required: 4 to 8 Years

  
  


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  Compensation (Market Standard): INR 2,400,000 to 3,800,000 Per Annum (CTC)

  
  


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  Locations: Bangalore / Pune / Hybrid / Remote-flexible across India

  
  

Role Overview & Key Responsibilities

We are looking for a talented AI/ML Data Scientist to build predictive analytical models, time-series forecasting engines, and machine learning solutions that drive enterprise decision-making. In this role, you will analyze complex, large-scale datasets, engineer powerful predictive features, and collaborate with software engineering teams to embed statistical and machine learning models directly into customer-facing software products.

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  Model Development: Design, train, evaluate, and optimize machine learning and deep learning models for classification, regression, clustering, and predictive forecasting.

  
  


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  Feature Engineering: Extract, clean, transform, and engineer high-value predictive features from massive unstructured and structured datasets.

  
  


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  ML Pipeline Automation: Build robust, end-to-end data ingestion and model training pipelines utilizing MLOps best practices and automated retraining scripts.

  
  


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  Model Validation & Tuning: Conduct rigorous hyperparameter tuning, cross-validation, and bias-variance analysis to ensure production models maintain accuracy and stability.

  
  


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  Business Insights Translation: Translate complex statistical findings and predictive metrics into clear visual dashboards, strategic reports, and actionable business recommendations.

  
  

Required Skills & Experience

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  Educational Background: Master’s or Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative discipline.

  
  


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  Technical Proficiency: Expert-level coding skills in Python, utilizing core data science libraries such as Pandas, NumPy, Scikit-Learn, PyTorch, or TensorFlow.

  
  


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  Statistical Expertise: Strong foundation in probability, statistical testing, hypothesis validation, and time-series modeling methodologies.

  
  


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  Collaboration Tools: Experience working with SQL databases, Git version control, and cloud-hosted data platforms (Snowflake, BigQuery, or AWS Redshift).