We are seeking a Senior/Lead Data Scientist with deep expertise in Data Science, Machine Learning, and Deep Learning to drive innovation and build advanced AI/GenAI/Agentic AI based solutions.

This role involves leading high-impact projects, mentoring teams, and shaping the AI/ML strategy, including contributions to Generative AI applications.

The ideal candidate is both a hands-on technical expert and a thought leader who can bridge the gap between complex AI research and real-world business applications.

Key Responsibilities :

- Lead the design, development, and deployment of end-to-end ML/AI models for solving complex business problems.

- Drive deep learning model development (CNNs, RNNs, Transformers, etc.) for diverse domains such as NLP, vision, and multimodal applications.

- Experience in Machine Learning, Deep Learning, Python, Tensorflow, Pytorch, Scikit-learn, Time-Series forecasting, MLOps.

- Collaborate with cross-functional teams including data engineering, product management, and business stakeholders to translate needs into scalable ML solutions.

- Mentor junior data scientists and contribute to building best practices, reusable frameworks, and a culture of innovation.

- Stay ahead of emerging ML research and proactively evaluate new tools.

Requirements :

Required Qualifications :

- Bachelor or Master Degree in Computer Science, Data Science, or related field.

- 5-8 years of hands-on experience in Data Science, ML, and Deep Learning.

- Strong expertise in Python, TensorFlow, PyTorch, and Scikit-learn.

- Solid foundation in mathematics, statistics, optimization, and probability theory.

- Proven track record of building and deploying production ML models at scale.

- Experience with cloud platforms (GCP, AWS, or Azure) for ML workflows.

- Strong problem-solving, leadership, and communication skills.

Good-to-Have Skills :

- Knowledge of LLM internals and architectures (transformers, attention mechanisms).

- Experience with GenAI ecosystems (LangChain, LlamaIndex, vector databases, RAG pipelines).

- Experience with prompt engineering.

- Exposure to MLOps practices model monitoring, CI/CD for ML, experiment tracking.

- Understanding of responsible practices bias detection, explainability, and governance.