Data Scientist

Actively Reviewing the Applications

Zorba AI

India, Tamil Nadu, Chennai Full-Time On-site INR 20–30 LPA

Posted 1 day ago • Apply by May 19, 2026

Job Description

Role Overview

We are looking for an experienced Data Scientist to drive AI/ML initiatives within the Digital Manufacturing domain. The ideal candidate will have strong expertise in machine learning, forecasting, and production-grade model deployment, with hands-on experience in Databricks and MLOps practices. This role requires close collaboration with business stakeholders to translate enterprise problems into scalable analytical solutions.

Must-Have Technical Competencies

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Economics, or related field.
8+ years of overall experience in Data Science (minimum 4–6 years delivering end-to-end forecasting or business-focused ML solutions).
Strong proficiency in Python and SQL.
Hands-on experience with Databricks for analytics, ML model development, and data pipelines.
Strong expertise in:

Time-series forecasting
Regression models
Classical machine learning techniques
Anomaly detection & early warning systems

Experience deploying ML models in production environments.
Strong understanding of MLOps practices (MLflow, Model Registry, CI/CD, monitoring, lifecycle management).
Excellent communication and stakeholder management skills.

Good-to-Have Competencies

Experience working with business functions such as sales, finance, procurement, treasury, or quality analytics in manufacturing/enterprise environments.
Exposure to Generative AI / LLM use cases (narrative generation, scenario simulation, hybrid modeling).
Experience in product-centric environments with cross-functional collaboration.
Knowledge of visualization tools like Power BI or Tableau.
Understanding of manufacturing, automotive, or large enterprise business processes.

Key Responsibilities

Collaborate with business teams to translate domain problems (e.g., spend analytics, sales forecasting, cash flow projections, quality trends) into analytical use cases.
Own the full ML lifecycle from data preparation and feature engineering to model development, testing, deployment, and monitoring on Databricks.
Develop time-series forecasting, regression, anomaly detection, and early-warning models for leadership reporting and strategic decision-making.
Identify and implement GenAI/LLM approaches where they add measurable business value.
Work closely with Data Engineering teams to ensure high-quality and reliable data pipelines.
Present analytical insights and recommendations clearly to executive stakeholders.
Implement MLOps best practices for reproducibility and operational stability.
Contribute to the organization’s AI/ML maturity by defining standards, reusable patterns, and lifecycle improvements.

Skills: data science,ml,forecasting