Lead Data Scientist

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:

• Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.
• Practical experience with hands-on modelling and experimentation.

• LLM & Generative AI:

• Conceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring.
• Exposure to vector databases
• Executed at least 1 impactful LLM/GenAI implementation in real-world settings.

• Programming:

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

• Deployment & Automation:

• Experience with cloud platforms (AWS/GCP/Azure)
• API integration and orchestration for scalable solutions.

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