Role & responsibilities :

- End-to-End Project Leadership : Take ownership of data science projects, from initial research and experimentation to scalable deployment, monitoring, and ongoing optimization.

- Strategic Leadership : Define the data science roadmap, align initiatives with business priorities, and influence senior leadership through data-driven insights.

- Team & Culture Building : Lead, grow, and inspire a high-performing team of data scientists, fostering innovation, collaboration, and continuous learning.

- Governance & Scaling : Establish best practices for responsible AI, ensure compliance with data regulations, and scale advanced analytics solutions across functions and geographies.

- Model Development : Design, build, and optimize advanced statistical and machine learning models to solve complex business problems.

- LLM & Generative AI : Research and implement innovative LLM/GenAI solutions, including advanced prompt engineering, Retrieval-Augmented Generation (RAG) frameworks, and parameter-efficient fine-tuning for specific business needs.

- Cross-functional Collaboration : Partner with product, engineering, and business stakeholders to translate complex challenges into actionable data science solutions and communicate findings effectively to technical and non-technical audiences.

- Mentorship : Guide and mentor junior data scientists and analysts, fostering a culture of technical excellence and continuous learning.

- Infrastructure & Deployment : Work with data engineering teams to design scalable data models and robust, automated ML pipelines using MLOps best practices and cloud services (AWS/GCP/Azure).

- Performance Monitoring : Implement monitoring frameworks to track and enhance the performance of deployed models.

Preferred candidate profile :

- Machine Learning & Statistics :

1. Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.

2. Practical experience with hands-on modelling and experimentation.

- LLM & Generative AI :

1. Conceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring.

2. Exposure to vector databases.

3. Executed at least 1 impactful LLM/GenAI implementation in real-world settings.

- Programming :

1. Proficiency in Python, R, and SQL for model development and data analytics.

- Deployment & Automation :

1. Experience with cloud platforms (AWS/GCP/Azure).

2. API integration and orchestration for scalable solutions.

3. Practical experience with MLOps tools like MLflow, Docker, or Kubernetes for CI/CD pipelines.