Principal Data Scientist - AI & Machine Learning

This position is not for urgent hiring.

Integrant is looking for game changers to join our team as "Data Scientist - AI & Machine Learning" with below roles and responsibilities:

• Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
• Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
• Conduct open research and experimentation to develop innovative solutions for complex client challenges.
• Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
• Design and implement end-to-end Machine Learning and Generative AI solutions.
• Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
• Develop scalable model deployment and monitoring solutions using MLOps best practices.
• Monitor model performance, detect concept drift, and continuously improve deployed systems.
• Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
• Mentor and coach junior Data Scientists and Machine Learning Engineers.
• Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
• Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.

RequirementsEducation & Experience

• 7+ years of professional experience, including 5+ years in Data Science, Machine Learning & MLOps
• MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• Experience mentoring, coaching, or leading technical team members.

Data Science & Machine Learning

• Strong foundation in Machine Learning techniques including Classification, Regression, Clustering, Association Rule Mining, Feature Engineering, and Model Evaluation.
• Experience with Deep Learning concepts and frameworks.
• Extensive hands-on experience with Python and the Data Science ecosystem.
• Experience with one or more ML frameworks such as Scikit-Learn, TensorFlow, Keras, or PyTorch.
• Experience conducting research, experimentation, and hypothesis-driven analysis.

MLOps & Production AI

• Experience deploying and managing Machine Learning models in production environments.
• Experience monitoring model performance, detecting concept drift, and driving continuous improvements.
• Hands-on experience with MLOps practices, CI/CD pipelines, model versioning, experiment tracking, monitoring, and observability.