Lead Data Scientist - Gen AI

In this strategic role, the Lead Data Scientist will steer the design and execution of complex data science initiatives, shaping technical strategy and mentoring teams. This role will involve driving innovation in AI/ML projects, establishing data and modelling standards, and collaborating cross-functionally to turn business challenges into analytical solutions.

Key / Primary Responsibilities:

- Leading the end-to-end lifecycle of advanced Gen AI projects, including scoping, solution design, delivery, and deployment within cross-functional teams.

- Defining data science best practices, code quality standards, and technical governance across the team.

- Expert-level proficiency in Python or R, with deep experience in data science libraries such as pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, and Keras.

- Overseeing the adoption and optimization of Natural Language Processing (NLP) solutions, including frameworks like NLTK, SpaCy, Gensim, as well as advanced transformer models (BERT, GPT, LLMs).

- Architecting and guiding the implementation of large-scale text analytics systems (topic modeling, sentiment analysis, text classification, semantic search).

- Ensuring robust model management, reproducibility, and effective MLOps in cloud (AWS, Azure, GCP) and on-premise infrastructures.

- Advanced SQL and NoSQL expertise, and hands-on use of orchestration and workflow automation tools (Airflow, Control-M).

Secondary Responsibilities:

- Mentoring and upskilling data scientists, reviewing code and solutions, and providing technical leadership on multiple concurrent projects.

- Shaping data flow designs for large-scale, unstructured data; championing data quality and integrity standards.

- Driving model validation, monitoring, and continuous improvement for production workloads.

Managerial & Leadership Responsibilities:

- Lead and mentor the data science team, setting strategic direction and fostering a collaborative, high-performance culture.

- Oversee planning, execution, and delivery of multiple data science projects, ensuring alignment with business objectives and timely completion.

- Serve as primary liaison with stakeholders, translating business needs into actionable data strategies and maintaining data governance standards.

Key Success Metrics:

- Timely and on-budget delivery of high-impact, production-ready models.

- Enabling and accelerating innovation in technical projects.

- Measurable improvements in team capability and modeling infrastructure reliability.

- Business value generation through advanced analytics solutions.