Senior Data Scientist

The Senior Data Scientist will be responsible for designing, developing, and deploying advanced data science and GenAI solutions across key business functions such as Operations, Pricing, and Sales. This role requires close collaboration with business stakeholders to deeply understand challenges, translate them into scalable AI/ML and GenAI solutions, and drive end-to-end execution from problem framing and model development to deployment and continuous monitoring in production environments.
The individual will also play a key role in mentoring junior data scientists, ensuring best practices in model development, and fostering a culture of innovation and technical excellence. Additionally, the role involves measuring business impact, communicating insights effectively, and demonstrating tangible value delivered through AI-driven initiatives.
Responsibilities:
Cross-Functional Collaboration: Partner with business, operations, pricing, and sales teams to identify and prioritize high-impact opportunities for ML, AI, and GenAI-driven solutions.
End-to-End Project Ownership: Lead data science initiatives from problem definition and ideation through development, deployment, and business adoption, ensuring timely and high-quality delivery.
Solution Architecture: Design scalable, robust, and production-ready ML and GenAI solutions aligned with business objectives, with a strong focus on measurable impact and ROI.
Hands-On Model Development: Build, validate, and optimise machine learning and AI models using state-of-the-art techniques, ensuring adherence to best practices in model development and experimentation.
Stakeholder Communication: Translate complex analytical outputs into clear, actionable insights and present recommendations effectively to senior stakeholders and leadership teams.
Deployment and Integration: Collaborate with data engineering and platform teams to operationalise models and integrate AI solutions seamlessly into business workflows and systems.
Performance Monitoring and Optimisation: Establish monitoring frameworks to track model performance, ensure reliability, and drive continuous improvement through retraining and refinement.
Mentorship and Team Leadership: Mentor and guide junior data scientists and analysts, fostering technical excellence, structured problem-solving, and a culture of continuous learning.
Requirements:
Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field (BE/BTech/MSc/MTech/PhD), with 5-6 years of relevant experience in Data Science, Machine Learning, and AI/GenAI solution development.
Technical Proficiency: Strong programming skills in Python and SQL, with hands-on experience in building end-to-end data science solutions. Familiarity with modern ML frameworks and libraries is expected.
AI/ML and GenAI Expertise: Proven experience across Machine Learning, Deep Learning, and Generative AI, with a strong track record of translating business problems into scalable, real-world AI solutions.
Cloud and Data Engineering Skills: Practical experience with cloud platforms (preferably AWS), along with exposure to big data technologies such as Spark. Working knowledge of scalable data pipelines and MLOps practices is a plus.
Data Management and Databases: Strong understanding of structured and unstructured data, with hands-on experience in SQL and NoSQL databases, and efficient data processing and retrieval techniques.
Analytical and Problem-Solving Skills: Strong analytical thinking with the ability to break down complex problems, apply structured approaches, and deliver innovative, data-driven solutions.
Communication and Stakeholder Management: Excellent communication and storytelling skills, with the ability to translate complex technical concepts into clear business insights for both technical and non-technical stakeholders.

Experience
5-8 yrs