Lead Data Scientist - Machine Learning & Generative AI

Description :

We are seeking an experienced and highly motivated Lead Data Scientist Machine Learning & Generative AI to drive enterprise-scale AI and ML initiatives. The ideal candidate will possess deep expertise in Machine Learning, NLP, LLMs, Generative AI, and RAG application development, along with strong experience in designing, deploying, and scaling production-grade AI solutions.

The candidate will work closely with business stakeholders, data engineers, and software engineering teams to solve complex business problems using advanced analytics, machine learning, and AI technologies. This role also involves technical leadership, mentoring teams, code reviews, and defining AI/ML best practices across the organization.

Key Responsibilities :

- Partner with business stakeholders to understand business challenges and translate them into scalable Machine Learning and AI solutions.

- Design, build, deploy, and optimize end-to-end machine learning and Generative AI applications in production environments.

- Develop and implement NLP, LLM, and RAG-based solutions for enterprise use cases.

- Coordinate with business teams to monitor outcomes, evaluate model performance, and continuously refine ML models.

- Work across supervised, unsupervised, reinforcement learning, and deep learning techniques to deliver optimal solutions.

- Lead the development of scalable AI frameworks and reusable components leveraging APIs and cloud platforms.

- Collaborate with Data Engineering and Software Engineering teams to ensure scalable and reliable ML model deployments.

- Own and drive code review processes while enforcing coding standards, documentation, and quality assurance best practices.

- Mentor and guide Data Scientists and ML Engineers in selecting appropriate algorithms, frameworks, and modelling techniques.

- Lead data mining, feature engineering, data preparation, and data quality initiatives across business use cases.

- Build enterprise-grade AI/ML pipelines using cloud-native technologies and MLOps practices.

- Develop insightful dashboards and visualizations using Tableau, ELK, or similar tools for business stakeholders.

- Present technical concepts and AI-driven recommendations to both technical and non-technical audiences.

- Stay updated with advancements in AI, Generative AI, LLMs, and emerging open-source technologies.

- Ensure adherence to version control, governance, security, and documentation standards using Git/GitHub.

Required Skills :

Mandatory Technical Skills :

- Strong expertise in Python for Data Science

- Experience with R Programming for Data Science

- Strong foundation in Statistics and Probabilistic Modelling

- Hands-on experience in deploying Machine Learning models into production environments

Deep understanding of :

- Machine Learning Algorithms

- NLP (Natural Language Processing)

- Large Language Models (LLMs)

- Generative AI

- RAG (Retrieval-Augmented Generation)

- Experience with supervised, unsupervised, reinforcement, and deep learning techniques

- Expertise in data wrangling, cleansing, feature engineering, and dimensionality reduction

- Strong knowledge of vector algebra, statistical modelling, and probability theory

- Experience with exploratory data analysis (EDA) and hypothesis testing

Hands-on experience with :

- PyTorch

- TensorFlow

- Scikit-Learn

- Pandas

- Spark ML

Experience with cloud ML platforms such as :

- Amazon SageMaker

- Azure ML Studio

- Familiarity with vector databases and AI orchestration frameworks

- Strong understanding of APIs, microservices, and scalable AI architecture

- Knowledge of Git, GitHub, Markdown, CI/CD pipelines, and MLOps best practices

Preferred Skills :

- Experience with chatbot and conversational AI platforms

- Exposure to LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks

- Experience with Docker and Kubernetes

- Experience with data visualization tools such as Tableau, Power BI, ELK Stack

- Understanding of distributed computing and big data ecosystems

- Experience in cloud-native AI deployments and scalable infrastructure

Familiarity with SQL and NoSQL databases such as :

- MySQL

- Oracle

- SQL Server

- MongoDB

Educational Qualifications :

- Computer Science

- Statistics

- Mathematics

- Informatics

- Information Systems

- Data Science

- Or another quantitative discipline

Experience Requirements :

- 8 to 12 years of experience in Data Science and Machine Learning

- Proven expertise in solving real-world business problems using AI/ML techniques

- Mandatory hands-on experience in deploying ML/AI models into production environments

- Experience leading enterprise-scale AI initiatives and mentoring technical teams

Mandatory Skills :

- Python Data Science

- R Data Science

- Statistics Data Science

- Machine Learning

- NLP

- LLMs & Generative AI

- RAG Application Development

- PyTorch / TensorFlow

- Azure ML / SageMaker