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Birlasoft Limited

Sr Data Scientist AI and ML

Birlasoft Limited Pune, Maharashtra, India

IT Services and IT Consulting · 10,001+ employees

Sep 25
data-scientist Senior (5-10 yrs) Full-time India
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About the role

Design, develop, and deploy AI and machine learning models, perform statistical analysis, and build predictive models to generate actionable business insights. Create end-to-end ML pipelines and MLOps frameworks to operationalize, monitor, and govern models in production.

What they look for

Artificial Intelligence Machine Learning MLOps Statistical Modeling Predictive Analytics Python Data Ingestion Feature Engineering Model Training Model Validation Model Deployment Continuous Integration Continuous Delivery Model Monitoring Model Governance Advanced Analytics

Requirements

The role calls for strong expertise in AI, machine learning, and MLOps, along with a deep understanding of statistical modeling, predictive analytics, and Python-based data ecosystems. Experience with Databricks Mosaic AI and Snowflake Cortex is an added advantage.

Full description

Area(s) of responsibility

Role Overview

We are seeking a highly skilled Data Scientist with strong expertise in Artificial Intelligence (AI), Machine Learning (ML), and MLOps. The ideal candidate will be responsible for building scalable predictive models, driving advanced analytics, and operationalizing ML models in production environments. This role requires a deep understanding of statistical modeling, predictive analytics, and Python-based data ecosystems, with exposure to modern platforms such as Databricks Mosaic AI and Snowflake Cortex being an added advantage.

Key Responsibilities

  • Design, develop, and deploy machine learning and AI models for real-world business problems.
  • Perform advanced statistical analysis and build predictive models to derive actionable insights.
  • Develop and implement end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and deployment.
  • Build and manage MLOps frameworks for continuous integration, delivery, monitoring, and model governance.

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