Data Scientist
Posted Updated
Summary
Senior data scientist (8-13 yrs) at Lloyds' Hyderabad technology centre, building end-to-end ML, NLP, and Generative AI solutions — including conversational and agentic AI with RAG and LangChain/LangGraph — plus deploying models via MLOps (MLflow, Docker, CI/CD) and mentoring other data scientists.
- Senior Data Science Expertise
Strong experience applying machine learning, statistical modelling, and experimentation to solve real business problems. - Advanced NLP & GenAI Capability
Deep theoretical and applied knowledge of NLP, blending traditional NLU approaches with modern LLMs and Generative AI techniques. - Conversational & Agentic AI Systems
Experience building conversational AI solutions, including agentic workflows, orchestration, and memoryaware systems. - Business Translation & Influence
Proven ability to translate complex data science outputs into clear insights for nontechnical audiences, influencing senior decisionmakers. - Strong Engineering & Coding Skills
Advanced Python and query language, API proficiency, writing modular, maintainable code; fluent with pandas and common DS libraries. - Coaching & Technical Leadership
Ability to coach other data scientists, review code, and guide teams toward effective, businessaligned solutions. - Modern Software & Delivery Practices
Experience working with DevOps, CI/CD, and modern development toolchains to productionise ML and AI solutions. - Collaboration & Stakeholder Management
Excellent communication and collaboration across engineering, business teams, and thirdparty SaaS providers.
What We Need (MustHave)
- Handson Modelling & Experimentation
Practical experience designing experiments and applying complex statistical methods endtoend. - Advanced Python & ML Frameworks
Strong proficiency in Python and libraries such as NumPy, Pandas, scikitlearn, PyTorch / TensorFlow. - EndtoEnd ML & GenAI Design
Strong understanding of feature engineering, model selection, evaluation, optimisation, and GenAI approaches.
Experience with modern model evaluation and testing approaches for ML and Generative AI, including ML, LLM evaluation, robustness testing, bias and safety checks, and continuous quality monitoring.
- Agentic AI & Orchestration
Experience with RAG, prompt and reasoning design, memory management, and tools such as LangChain / LangGraph. - CloudBased AI Deployment
Good Familiarity with for deploying microservices, ML and AI workloads. Experience with MLOps tools (e.g. MLflow, Docker, CI/CD pipelines) - BusinessReady Delivery
Ability to take complex business requirements and turn them into scalable, productionready AI solutions. - Strong Communication & Influence
Confidence explaining technical outcomes in business terms and influencing nontechnical stakeholders.