Senior Data Scientist - Artificial Intelligence/Machine Learning

About The Role

Key Responsibilities

• Translate complex business problems into data science solutions by working closely with business stakeholders, product teams, and domain experts.
• Conduct problem scoping, solution framing, and feasibility assessments to define clear ML project goals and success metrics.
• Perform in-depth exploratory data analysis (EDA) to uncover hidden insights and relationships in large, high-dimensional datasets.
• Design and implement robust data preprocessing and feature engineering pipelines, using both structured and unstructured data.
• Develop and validate scalable, production-grade ML models including traditional ML (Random Forests, Gradient Boosting, SVM) and deep learning models (CNNs, RNNs, Transformers).
• Deploy, monitor, and maintain ML models in production environments using Azure ML (preferred), AWS Sagemaker, or Google Vertex AI.
• Implement end-to-end ML pipelines integrating data ingestion, preprocessing, training, evaluation, deployment, and monitoring (MLOps).
• Write clean, reusable, and well-documented code in Python following industry standards and MLOps principles (e.g., CI/CD, model versioning, automated testing).
• Use version control (e.g., Git), experiment tracking tools (e.g., MLflow), and containerization (Docker/Kubernetes) where applicable.
• Present technical concepts, insights, and recommendations to business and technical stakeholders in a clear and effective manner.
• Lead client engagements and influence decision-making through storytelling, data visualization, and domain-specific insights.
• Mentor and guide junior data scientists on technical approaches, project planning, and model evaluation.
• Foster a culture of innovation, collaboration, and continuous learning across cross-functional teams.
  

Qualifications

• Bachelors or Masters degree in Metallurgy, Mechanical Engineering, Computer Science, Information Technology, or related disciplines from a top-tier institution (IIT/NIT or equivalent).
• 4+ years of hands-on experience in data science, machine learning, or applied AI.
• Prior work in manufacturing, metallurgy, or industrial domains is highly desirable.
• Proven track record of building and deploying machine learning solutions at scale