Job Description

Lead Data Scientist

The Lead Data Scientist will drive the design, development, and deployment of advanced analytics and machine learning solutions to solve complex business problems. This role combines deep technical expertise with hands-on delivery leadership, working closely with clients and cross-functional teams to translate business needs into scalable AI/ML solutions. The role will also leverage emerging techniques such as Generative AI where relevant to enhance solution effectiveness. The Lead Data Scientist will mentor junior team members and contribute to strengthening the organization’s AI/ML capabilities.

Responsibilities

AI/ML Solution Development & Delivery

• End-to-End Model Development: Design, build, and deploy machine learning and statistical models, from problem framing to productionization.
• Advanced Analytics: Apply techniques across predictive modeling, optimization, simulation, and statistical analysis to drive business outcomes.
• Generative AI Integration: Apply Generative AI approaches (e.g., LLM-based solutions, embeddings, prompt engineering) for use cases such as knowledge extraction, automation, and content generation where appropriate.
• Model Operationalization: Collaborate with engineering teams to integrate models into production systems, ensuring scalability and performance.
• MLOps Alignment: Follow best practices for model versioning, monitoring, and lifecycle management, including emerging practices for LLMOps.

Technical Leadership

• Solution Design: Translate business problems into analytical frameworks and technical solutions, including hybrid approaches combining classical ML and Generative AI where relevant.
• Code & Model Quality: Ensure high standards for code, reproducibility, and model robustness.
• Experimentation: Lead hypothesis-driven experimentation and guide teams on feature engineering, model selection, evaluation, and prompt tuning.
• Innovation Adoption: Stay current with emerging AI/ML and Generative AI techniques and assess their applicability to client problems.

Client Engagement

• Problem Framing: Work with client stakeholders to understand business challenges and define AI/ML and data-driven use cases.
• Insight Communication: Present findings, model outputs, and recommendations in a clear and actionable manner.
• Solution Demonstration: Build PoCs and prototypes, including GenAI-led experiences (e.g., conversational interfaces, copilots), to showcase value and accelerate adoption.

Team Mentorship

• Guidance: Mentor junior data scientists and analysts on modeling techniques, tools, and best practices, including responsible use of Generative AI tools.
• Peer Reviews: Conduct code reviews and provide technical feedback to ensure quality and consistency.
• Capability Building: Contribute to internal knowledge sharing, reusable assets, and accelerators, including GenAI components.

Contribution to Practice Development

• Reusable Assets: Develop and enhance reusable components such as feature engineering pipelines, model templates, prompt libraries, and domain-specific solutions.
• Thought Contribution: Contribute to blogs, whitepapers, or internal artifacts on AI/ML and emerging GenAI trends.
• Pre-sales Support: Assist in solutioning, proposal development, and client discussions where required, including GenAI-led opportunities.

Requirements

Education and Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or related field.
• 8–12 years of experience in data science or AI/ML roles with strong hands-on delivery experience.

Technical Skills

• Strong expertise in machine learning, statistical modeling, and optimization techniques
• Proficiency in Python (preferred) or R, and common ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Working knowledge of Generative AI concepts (LLMs, embeddings, vector databases, prompt engineering, RAG).
• Experience working with data platforms, large-scale data processing (e.g., Spark), and cloud environments.
• Familiarity with MLOps practices, model deployment, monitoring frameworks, and exposure to LLMOps concepts.

Core Competencies

• Problem Solving: Ability to break down complex business problems into analytical solutions.
• Analytical Thinking: Strong foundation in statistics, experimentation, and data-driven decision making.
• Communication: Ability to explain complex models and insights to both technical and non-technical audiences.
• Collaboration: Experience working with cross-functional teams including data engineers, product teams, and business stakeholders.

Preferred Qualifications

• Experience in travel, transportation, or logistics domains (airlines, hospitality, OTAs, etc.).
• Exposure to optimization, simulation models, or decision science techniques
• Hands-on experience with LLM-based applications or GenAI frameworks (e.g., LangChain, vector DBs, copilots).
• Contributions to open-source projects, research papers, or patents