JOB DESCRIPTION We are seeking a highly skilled GCP Data Scientist with expertise in cloud infrastructure, LLM frameworks, and MLOps to join our team. The role involves working on predictive modelling, data discovery, model evaluation, and scalable data science workflows for assigned use cases. Key Responsibilities: Data Discovery and Preparation: Explore available datasets and assess their relevance, completeness, quality, and suitability for modelling. Identify data quality limitations, missing information, potential bias, and other factors affecting analytical validity. Develop reproducible data cleaning, transformation, and feature engineering logic. Work with data and domain teams to clarify data definitions, assumptions, and dependencies. Ensure data preparation follows security, privacy, and governance requirements. Predictive Modelling: Develop, evaluate, and improve predictive, forecasting, and recommendation models for assigned use cases. Select modelling approaches appropriate to the available data, required outputs, and operational constraints. Establish suitable baseline approaches against which model improvements can be assessed. Conduct structured experimentation, model comparison, and parameter optimisation. Clearly document modelling assumptions, design decisions, and known limitations. Model Evaluation and Quality Assurance: Define appropriate validation strategies, evaluation datasets, and performance measures. Test model performance across relevant data segments, scenarios, and time periods. Assess model reliability, uncertainty, stability, and sensitivity to changes in input data. Analyse model failures and distinguish modelling issues from data quality or evaluation data issues. Produce traceable evaluation evidence sufficient for technical and business acceptance reviews. Scalable Data Science Workflows: Develop reproducible data preparation, model training, inference, and scoring workflows using approved cloud tooling. Structure code and configuration to support repeatable experimentation, testing, and future maintenance. Collaborate with engineering teams to prepare model components for integration into downstream platforms. Support implementation of appropriate model monitoring, performance tracking, and retraining processes. Ensure implementation follows coding, version control, peer review, and maintainability standards. Documentation, Communication and Handover: Maintain clear technical documentation covering data preparation, features, modelling methods, evaluation, and limitations. Communicate analytical findings and recommendations effectively to technical and non-technical stakeholders. Maintain source-controlled notebooks, code, configuration, and supporting artefacts. Participate in technical reviews, stakeholder playback sessions, and knowledge-sharing activities. Conduct knowledge transfer to data scientists and engineers where required. REQUIREMENTS Core Skills: GCP expertise (Cloud Run, Cloud Build, Vertex AI, Big Query, IAM) Deploying and managing web/API services Experience with LangGraph or similar agent frameworks LLM evaluation and assessment MLOps and AI platform operations Nice to Have: Classical machine learning modelling Data mining and statistical analysis Building predictive models from scratch Traditional machine learning algorithms Model training and feature engineering Experience: 11 to 15 years Duration: 15-Oct-26 to 31-Dec-2026 Location: Bengaluru (preferred) or any location in India Working Mode: Hybrid – 3 days from office Client Interview: Yes