Principal Engineer, Data Science

REQUIREMENTS: Total experience 11+ years. Minimum 9+ years in data science, ML engineering, with at least 5 years leading ML solutions from design to production. Should have experience in software engineering, with strong depth in Python. Predictive modelling: regression-based estimation with explainable drivers, prediction intervals and live "what-if" re-scoring. Similarity and retrieval: hybrid vector and structured-attribute search. Hierarchical analytics: scoring, sizing and ranking opportunities across multi-level hierarchies. Should have exposure to a conversational AI assistant with tool calling, grounded and cited answers, and guardrails. Must have production experience with regression and tabular ML: feature engineering, model selection, calibration, uncertainty (conformal or quantile intervals) and explainability (SHAP, coefficients). Should have hands-on experience with embeddings, vector search and hybrid retrieval, and with LLM applications: tool calling, retrieval-grounded generation, citations, guardrails and evaluation. Should have hands-on experience for building conversational agent, with multi-turn session memory and tool calling into the multiple engines and the semantic layer. Should have experience on a major cloud: pipelines, model registry, drift monitoring, retraining and low-latency serving. Experience of Integrating with BigQuery, the semantic layer (LookML) and the existing React and backend-for-frontend application. Must have hands-on experience with tree-based models (XGBoost or LightGBM) and regression, model evaluation, and explainability (SHAP). Should have practical LLM experience: prompting, tool calling, retrieval-augmented generation, embeddings and vector search. Should have experience with ML pipelines and model deployment on a major cloud, and with Git, testing and code review. Should have strong statistics and metrics knowledge, including error metrics. Must have google cloud experience (Vertex AI, BigQuery, Gemini, Vector Search), or an equivalent cloud (AWS/Azure). Should have experience with agent frameworks, prompt-injection testing and LLM evaluation methods. Should have experience with semantic layers such as Looker, and with enterprise AI governance. Should have exposure to cost, pricing or supply-chain analytics. Must have ability to translate ambiguous business problems into durable technical architectures. Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership. RESPONSIBILITIES: Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements. Set modelling standards: how models are compared and chosen, validation and holdout rules, explainability, uncertainty and acceptance criteria. Lead feature engineering and model selection, and hands-on build of the hardest models and retrieval pipelines. Design the GenAI agent: tool calling, retrieval-grounded generation, session memory, guardrails, human approval for actions and layered prompt-injection protection. Define evaluation: golden test sets, groundedness, tool-routing accuracy and release gates, and run regular quality reviews. Define serving and MLOps practice with the architect and DevOps: model registry, pipelines, drift monitoring, retraining and rollback. Apply Responsible AI practice: bias checks, auditability, conversation logging and retention, and clear explanations for business users. Estimate work, track risks and dependencies, and report status to the delivery lead. Present technical results to business and technical audiences Partner with product, design, data, and business leaders to translate strategic objectives into scalable technical solutions. Review critical designs and codebases, unblock teams on complex technical challenges, and raise the overall engineering bar. Lead technical discovery and solutioning for high-impact initiatives, including client or executive-facing workshops when required. Ensure enterprise readiness: security, privacy, compliance, governance, and responsible AI practices. Mapping decisions with requirements and be able to translate the same to developers. Identifying different solutions and being able to narrow down the best option that meets the client’s requirements. Defining guidelines and benchmarks for NFR considerations during project implementation Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed. Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it Understanding and relating technology integration scenarios and applying these learnings in projects Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken. Carrying out POCs to make sure that suggested design/technologies meet the requirements. Bachelor’s or master’s degree in computer science, Information Technology, or a related field.