Live opening · Posted 1 day ago
At a glance
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About the role
Description supplied by the original job listing.
The role involves owning the end-to-end AI/ML lifecycle, from business problem identification and model development to deployment, monitoring, and continuous improvement. The individual will work closely with business stakeholders to deliver scalable AI solutions with measurable business impact and contribute to the adoption of AI-driven decision-making across the organization.
Experience 5 to 10 years of relevant experience in Data Science, Machine Learning, and AI. Educational Qualification Mandatory: Full-time B.E./B.Tech. degree from a recognized institution. Postgraduate qualifications in Data Science, Analytics, AI/ML, or related fields would be an added advantage. Must-Have Skills Strong hands-on experience in Python and SQL. Experience building and deploying Machine Learning models in production environments. Strong understanding of: Predictive Analytics Feature Engineering Model Optimization / Hyperparameter Tuning Model Monitoring and Lifecycle Management Statistical Analysis Experience with algorithms/frameworks such as: XGBoost Random Forest Neural Networks / Deep Learning Ability to translate business problems into data-driven solutions and demonstrate measurable business impact. Strong stakeholder management and communication skills. Good-to-Have Skills PySpark / Big Data technologies. Cloud platforms such as AWS, Azure, or GCP. MLOps exposure including model deployment, monitoring, version control, and reproducibility. Exposure to GenAI, LLMs, NLP, or advanced analytics use cases. Experience mentoring junior team members or leading technical discussions. Preferred Industry
Experience First Preference: Manufacturing Domain Candidates who have either worked directly within manufacturing organizations or delivered Data Science solutions for manufacturing clients. Relevant use cases may include predictive maintenance, quality analytics, process optimization, demand forecasting, supply chain analytics, industrial IoT, and production planning. Relevant use cases may include customer analytics, demand forecasting, inventory optimization, pricing, recommendation engines, and supply chain analytics. Preferred Manufacturingand Retail candidates and not preferred BFSI domains, unless they also possess significant manufacturing or retail analytics exposure.
Skills: machine learning,python,sql
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