Senior Data Scientist

Job Summary

Senior Data Scientist to join our Bangalore office to expand the existing data science team. The data scientist will work in a relatively flat team structure with an independent goal of building and manage critical data science models daily. You are expected to solve hard technical problems and grow into an expert on batch, real-time, and LLM-driven Data Science use cases. Individual contributor role with 5+ years of experience expected.

The Role

• Develop an understanding of Financial Services businesses across regional markets to build scalable AI solutions. Main focus areas include payment optimization (e.g., method recommendations, downtime management), agent-native operations (e.g., automated insurance claims processing), and customer-facing conversational AI
• Leverage advanced LLMs to power the next generation of payment experiences.
• Manage the entire end-to-end lifecycle of predictive models and AI agents, from development to deployment and optimization.
• Work with engineering, compliance operation teams to create solutions and product changes informed by your findings and business inputs.
• Stay current with the latest AI/LLM research and new frameworks and incorporate them into practical applications.
• Or in a team to solve complex problem statements.
• Individual contributor role with 5+ years of experience expected.

The Daily Activities

• Predictive Modelling: Build predictive models using machine learning to solve for classification, ranking and online learning. Engineer features from internal data assets to build refined transaction and customer profiles.
• Generative AI Development: Design and deploy Generative AI solutions using prompt engineering, agent orchestration, and model fine-tuning (e.g., LoRA, PEFT). Build evaluation datasets and define guardrails for ongoing evaluation and monitoring.
• MLOps Automation: Build end-to-end MLOps pipelines to automate model training, deployment, and monitoring. Track production performance and implement feedback loops for continuous improvement.
• Ownership and Collaboration: Design AI solutions by working backwards from our needs. Lead the delivery of AI capabilities, from concept through production deployment.

The Must Haves

• Core ML: Expert proficiency in Python, Spark, and SQL (Presto/Hive) with a command of fundamental ML concepts (Bagging, Boosting, Online Learning, Recommendation Engines).
• Generative AI Agent Frameworks: Hands-on experience with Agentic frameworks (specifically LangGraph, LangSmith, or LangChain) and LLM orchestration.
• MLOps Productionization: Expertise in productionizing ML solutions with MLOps tools (MLflow, Kubeflow, SageMaker) and deploying scalable components on cloud platforms.

Strong adherence to clean coding, modular design, and version control (Git/GitHub) characterizes Engineering Excellence. This also involves the ability to balance model performance with business trade-offs, including latency, cost, and scalability.

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