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Birlasoft Limited

Sr Data Scientist

Birlasoft Limited Pune, Maharashtra, India

IT Services and IT Consulting · 10,001+ employees

20 h ago
data-scientist Senior (5-10 yrs) Full-time India
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About the role

The role involves designing, developing, and deploying machine learning and AI models to solve complex business problems. You will also build and manage MLOps frameworks while collaborating with data engineering teams to ensure scalable data pipelines.

What they look for

Artificial Intelligence Machine Learning MLOps Predictive Modeling Advanced Analytics Python Statistical Modeling Databricks Mosaic AI Snowflake Cortex Data Pipelines Exploratory Data Analysis Hypothesis Testing Feature Engineering Model Deployment Continuous Integration Continuous Delivery

Requirements

Candidates must possess strong expertise in AI, ML, and MLOps with a deep understanding of statistical modeling and Python-based ecosystems. Experience with modern platforms like Databricks Mosaic AI and Snowflake Cortex is considered a significant 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.
  • Work closely with data engineering teams to ensure robust and scalable data pipelines.
  • Conduct exploratory data analysis (EDA) and hypothesis testing to support data-driven decision-making.

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