Position Title	Sr. D&T Data Scientist	Function/Group	Digital & Technology
Location	Mumbai India	Shift Timing	Regular
Role Reports to	D&T Manager - Data Science	Remote/Hybrid/in-Office	Hybrid

ABOUT GENERAL MILLS

We make foodthe world loves: 100 brands. In 100 countries. Across six continents. With iconic brands like Cheerios, Pillsbury, Betty Crocker, Nature Valley, and Häagen-Dazs, we've been serving up food the world loves for 155 years (and counting). Each of our brands has a unique story to tell.

How we make our food is as important as the food we make. Our values are baked into our legacy and continue to accelerate

us into the future as an innovative force for good. General Mills was founded in 1866 when Cadwallader Washbur n boldly bought the largest flour mill west of the Mississippi. That pioneering spirit lives on today through our leadership team who upholds a vision of relentless innovation while being a force for good. For more details check out

General Mills India Center (GIC) is our global capability center in Mumbai that works as an extension of our global organization delivering business value, service excellence and growth, while standing for good for our planet and people.

With our team of 1800+ professionals, we deliver superior value across the areas of Supply chain (SC) , Digital & Technology (D&T) Innovation, Technology & Quality (ITQ), Consumer and Market Intelligence (CMI), Sales Strategy & Intelligence (SSI) , Global Shared Services (GSS) , Finance Shared Services (FSS) and Human Resources Shared Services (HRSS).For more details check out

We advocate for advancing equity and inclusion to create more equitable workplaces and a better tomorrow.

Job Overview

Role: Sr. D&T Data Scientist

Location: Mumbai, Hybrid

General Mills, Digital and Technology India, is seeking a Sr Data Scientist to join the Data Science team. Data Scientists within our team are dedicated to build enterprise intelligent automations and enable Core ML, Deep Learning, Generative AI solutions on various use cases in CPG. They are also responsible for curating a community of practice to determine the best standards and practices around data science at General Mills.

Role Responsibilities

Deliver on business problems:

Lead, design, and execute novel ways to help business partners achieve objectives through analysis and modelling.
Lead the design & implementation of Generative AI, Agentic AI, LLM & NLP solutions for unstructured text and images, structured data, covering problem framing, data curation, finetuning, and evaluation across classification, entity extraction & summarization.
Partner closely with AI Leadership, ML Engineering, and business stakeholders to architect highquality Agentic AI and RAG workflows, optimizing ingestion, chunking, embeddings and vector indexing with established frameworks and stores to maximize retrieval performance.
Design and maintain business semantics (ontologies, taxonomies, and metadata standards) so that Agentic AI and RAG systems retrieve and reason over information using consistent business concepts (brands, categories, customers, channels, plants, regions, etc.)
Create production ready, scalable models that provide real-time insights that align with General Mills technology standards.
Recognizes opportunities to apply external industry trends and implements them within role.

Consultation:

Provide technical leadership through strong partnership and by offering alternative solutions.
Provide technical leadership for analytical solution design by hypothesisdriven experimentation, defining robust evaluation strategies and error taxonomies with humanintheloop reviews, with clear documentation for transparency and reusability.
Lead Interactions with stakeholders to learn on problem statement and drive results.
Be an advocate for data science team.
Constructively challenge the other data scientists on approach
Contribute to best practices as we evaluate new platforms, tools and pipelines.
Coach Jr. Data Scientist / Interns / Contractors on key technical and domain topics through solution reviews and mentoring.
Collaborate with analytic leaders across functions.
Provide consultation and review the deliveries for other data scientists.

Stakeholder Management:

Manage the assigned priorities, consistency of execution and managing resources.
Act as a thought leader in data science techniques and represent the Digital and Technology organization.
Contributing towards Data Science technical strategy, best practices, and team development.
Champion Responsible AI by ensuring privacy, security, and governance compliance reducing model errors and bias and partnering across teams to integrate solutions into daily work.
Develop trust and credibility with business leade rs.
Educate stakeholders on the GCP analytic practices.
Closely collaborates with the stakeholders on projects and data science leaders to ensure practices are developed and enhanced to support accelerated analytic development and maintainability.

Collaboration:

Collaborates with ML engineers and systems engineers to ensure the models are deployed in a scaled and optimized way. Additionally ensure support the post-production to ensure model performance degrades are proactively managed.
Work closely with data science leadership, engineering, and business partners to develop machine learning models using best-in-class tools and technology.
Networks with senior internal and external personnel in own area of expertise.
Lead research work to new analytic trends aligned to business.
Leverage and contribute to new open-source innovation.

Must - have technical skills and experience

(Expert in section2 with exposure to section1)

Section1-Generative AI/Agentic AI

Proven experience in leading the design and implementation of complex data science solutions, particularly in LLM, NLP, Agentic AI, and RAG
Proficiency in designing and implementing LLM and NLP solutions, including problem framing, data curation, fine-tuning, and evaluation (classification, entity extraction, summarization).
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.
Ability to co-create production-ready, scalable models that provide real-time insights, adhering to technology standards (implies MLOps practices).
Expertise in hypothesis-driven experimentation, defining robust evaluation strategies, error taxonomies, and human-in-the-loop reviews.
Knowledge and application of Responsible AI principles, including privacy, security, governance compliance, bias reduction, and model interpretability.
Proficiency with best-in-class tools and technologies (e.g., Python, LangChain, Lang Graph, Google ADK, Google Cloud Platform)
Track record of creating models that are deployed in a production environment

Section-2- AI/ML

Expertise in supervised ML algorithms, regression, decision trees, ensemble models, time series, forecasting, neural networks
Proven implementation of ML and AI practices
Exposure to unsupervised learning and NLP
Technical concepts and platforms- MLOps, Containerization, Data Lineage and Visualization
Proficiency on Google Cloud Platform, SQL and Python

Other expertise and experience

Minimum qualification- bachelor's degree (full time)
Total analytics experience required 10-12 Years
Bachelor's or Master degree in computer science/ Artificial Intelligence/ Machine Learning /Statistics/Applied Mathematics from Tier 1 institute
Proficiency in FMCG/CPG domain
Experience working with an Agile development methodology featuring sprints, point estimation and daily stand-ups
Excellent stakeholder management skills and storytelling skills
Demonstrates learning agility and ability to apply to work.

Good to have skills:-

Knowledge of Machine Learning/Deep Learning/Operations Research

Skill proficiency expectations

Expert Level

Intermediate Level

Basic Level

. NLP, Generative AI, Agentic AI

. RAG Architecture

. Google Cloud Platform - Vertex AI, BQ, Composer

. FMCG/CPG domain

. Python, LangChain, ADK

. ML supervised algorithms

. Google Cloud Architecture

. MLOps

. Deep Learning