Data Scientist

Key Responsibilities

• Design, develop, and deploy AI-powered solutions leveraging Machine Learning, NLP, Generative AI, RAG, and Agentic AI techniques.
• Translate business problems into prototypes, MVPs, and scalable production-ready solutions.
• Develop reusable AI frameworks, components, and accelerators that enable adoption across products and teams.
• Apply an AI-first mindset to identify opportunities for intelligent automation, workflow optimization, and business value creation.
• Partner with Product, Engineering, Content, and Business teams to deliver impactful customer outcomes.
• Drive delivery through agile ways of working, balancing experimentation, speed, and engineering quality.
• Communicate insights, innovation outcomes, and business impact through effective data storytelling and stakeholder engagement.
• Mentor team members and contribute to knowledge sharing, innovation forums, and AI

Required Skills & Qualifications

• Experience in Data Science, Machine Learning, NLP, Applied AI, or AI Product Development.
• Proven track record of delivering AI solutions from experimentation through production deployment.
• Experience working within product-focused, cross-functional teams.
• Demonstrated ability to independently solve complex problems and deliver measurable business outcomes.

Technical Skills

• Strong expertise in Python and the modern Data Science ecosystem.
• Hands-on experience with Machine Learning, Deep Learning, NLP, and Statistical Modelling.
• Experience developing Generative AI applications using LLMs and modern AI frameworks.
• Strong understanding of RAG architectures, prompt engineering, embeddings, semantic retrieval, and vector databases.
• Exposure to Agentic AI and frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar technologies.
• Strong SQL and data engineering fundamentals.
• Experience building APIs and intelligent services using FastAPI or similar frameworks.
• Familiarity with Git, Docker, CI/CD, cloud platforms (Azure and/or AWS), and MLOps practices.
• Strong Computer Science fundamentals including data structures, algorithms, system design, and software architecture principles.

We are looking for individuals who:

• Demonstrate a Go-Getter mindset with strong ownership, accountability, and a bias for action.
• Thrive in a builder culture, rapidly progressing from idea to prototype, MVP, and scalable solutions.
• Embrace a fail-fast, learn-faster approach while maintaining high engineering and delivery standards.
• Build trusted relationships and influence outcomes through strong collaboration with business, product, and technology partners.
• Adapt quickly to changing priorities, emerging technologies, and evolving business needs.
• Apply an AI-first mindset to reimagine workflows, automate processes, and unlock business value.
• Communicate complex technical concepts through compelling data storytelling and clear business narratives.
• Articulate customer, operational, and commercial impact, helping drive adoption and scale of innovative solutions.
• Actively contribute to the Data Science community through knowledge sharing, collaboration, and continuous learning.