Senior Data Scientist

KEY ACCOUNTABILITIES

Lead the design and delivery of scalable data science solutions that solve high-value business problems across Core ML, Generative AI, and Agentic AI.

Partner with business stakeholders to frame problems, translate needs into measurable outcomes, and communicate insights and recommendations clearly.

Develop and evaluate ML models across regression, classification, forecasting, optimization, NLP, and deep learning, apply robust experimentation, evaluation, and error-analysis practices.

Design and implement Agentic AI, LLM, NLP, and RAG solutions, including data curation, ingestion, chunking, embeddings, vector indexing, retrieval optimization, fine-tuning, and human-in-the-loop evaluation.

Define and maintain business semantics, including ontologies, taxonomies, and metadata, to enable consistent retrieval and reasoning across enterprise information.

Collaborate with ML engineers and systems engineers to deploy, monitor, and improve production models, proactively address performance degradation and maintainability through MLOps practices.

Champion Responsible AI by applying privacy, security, governance, transparency, and bias-reduction practices throughout solution design and delivery.

Provide technical leadership through solution reviews, consultation, and constructively challenge other data scientist on the approach.

Contribute to best practices as we evaluate new platforms, tools, and pipelines.

Apply sound judgment in selecting methods and techniques for complex, diverse business problems.

Build connections with senior internal and external experts to bring relevant perspectives into solution design.

Build trusted stakeholder relationships, manage priorities and delivery commitments, and represent Data Science as a thought partner across functions.

Mentor junior data scientists, interns, and contractors and support their technical and domain development.

Stay current on industry trends, lead applied research aligned to business needs, and contribute to relevant open-source innovation.

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MINIMUM QUALIFICATIONS

10-12 years of relevant analytics or data science experience, with a demonstrated track record of delivering production ML and AI solutions.

Bachelor’s orMaster’s degree in computer science, Artificial Intelligence, Machine Learning, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, or a related quantitative discipline from a Tier 1 institute.

Strong expertise in supervised machine learning, including regression, decision trees, ensemble methods, time series, forecasting, neural networks, and model performance tuning, exposure to unsupervised learning and NLP.

Strong understanding of production ML practices, including MLOps, containerization, data lineage, model monitoring, and visualization.

Proficiency in Python, SQL and Google Cloud Platform.

Experience with Agile delivery practices, including sprints, estimation, and daily stand-ups.

Strong stakeholder management, consulting, communication, and storytelling skills, with the ability to influence technical and non-technical audiences.

Experience in the FMCG/CPG domain, supply chain analytics experience is preferred.

PREFERRED QUALIFICATIONS

Proven experienceinleading the design and implementation of complex end-to-end data science solutions using Generative AI, LLM, NLP, Agentic AI, or RAGfrom business problem framing and data curation through fine-tuning, evaluation, and production deployment.

Strong understanding and experience in architecting high-quality Agentic AI and RAG workflows, including data ingestion, chunking, embeddings, vector indexing, and optimizing retrieval performance using established frameworks and vector stores.

Expertise in hypothesis-driven experimentation, defining robust evaluation strategies, error taxonomies, and human-in-the-loop reviews.

Hands-on experience with GenAI tooling such as Vertex AI, LangChain, LangGraph, Google ADK, embeddings, or vector databases.

Experience with deep learning, optimization, or operations research.

Experience with GCP architecture and enterprise-scale AI solution design.

Knowledge of cybersecurity fundamentals and Responsible AI governance.




ELIGIBILITY

Applicants must meet minimum age qualifications in the country in which the job is located.