New opportunity

Consultant - Advanced Analytics, GIC SC Analytics

General Mills · Mumbai, India

About this role

The Supply Chain Analytics group at General Mills is seeking a Consultant for Advanced Analytics to join the Segment Analytics team in Mumbai. This individual contributor role focuses on developing innovative supply chain analytics solutions using descriptive, diagnostic, predictive, and prescriptive modeling, statistical analysis, and operations research techniques. The position includes project leadership, stakeholder management, mentoring, and operating in a 1.30 PM to 10.30 PM shift.

Responsibilities

Partner with supply chain stakeholders to understand business decisions, constraints, and objectives, translating ambiguous questions into clear analytical problems, hypotheses, and scope.
Select appropriate analytical approaches such as machine learning, statistical analysis, forecasting, or operations research while assessing feasibility and risks.
Ideate and execute improvements using historical data analysis and machine learning to build scalable descriptive, diagnostic, predictive, and prescriptive models.
Deploy model recommendations, provide actionable insights through visualizations, and support enhancements and bug fixes using DevOps and MLOps practices.
Mentor and train team members in data preparation, analytics techniques, R, Python, and supply chain analytics while standardizing processes and frameworks.

Required qualifications

Full-Time graduation from an accredited university.
At least 4 years of relevant experience in analytics, data science, machine learning, or a closely related field.
Proficiency in R, Python, intermediate SQL, Excel, and Power BI.
Strong understanding of end-to-end supply chain operations and experience across descriptive, diagnostic, predictive, and prescriptive analytics.
Strong problem-solving, project management, communication, and collaboration skills.

Preferred qualifications

Master’s degree in Operations Research, Analytics, Mechanical, Industrial or Production Engineering, or Statistics.
5+ years of relevant experience.
Experience working with DevOps, MLOps, Git, and GCP.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

RPythonSQLExcelPower BIDevOpsMLOpsGitGCPMachine LearningOperations Research

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Translating Ambiguous Supply Chain Requirements into Scoped Analytical Models
Partner directly with supply chain stakeholders to capture business decisions, operational constraints, and objectives. Translate ambiguous questions into structured hypotheses, clear problem definitions, and measurable success criteria while selecting appropriate modeling or operations research techniques.
Designing and Scaling Descriptive, Predictive, and Prescriptive Analytics Solutions
Ideate and execute advanced analytics improvements through historical data analysis and machine learning. Build robust analytical models incorporating feature engineering and automated testing to drive operational efficiency.
Deploying Models and Providing Actionable Insights via Visualizations
Translate complex model findings into clear presentations and visual dashboards. Ensure successful deployment, documentation, and maintenance of applications using DevOps and MLOps practices on cloud infrastructure like GCP.
Coaching and Mentoring Junior Analysts
Dedicate time to mentor and train team members in data preparation, diagnostics, predictive analytics, coding practices in R and Python, and supply chain analytics domain knowledge while contributing to team standards.

Likely challenges to think through

Balancing Individual Technical Delivery with Mentorship and Stakeholder Management
The role requires splitting time across requirement gathering, hands-on model building, deployment, and team training within an individual contributor framework. The difficulty lies in maintaining deep technical output while supporting analysts and aligning with cross-functional supply chain stakeholders. Prepare by demonstrating strong organizational skills, time-blocking frameworks, and structured delegation practices during team coaching.
Managing Shift Timing Constraints for Global Collaboration
Operating in a 1.30 PM to 10.30 PM shift requires coordinating with global supply chain partners while handling local mentoring and project delivery responsibilities. The trade-off involves maintaining high energy and focus during afternoon-to-evening hours. Prepare by establishing predictable communication cadences and maintaining effective personal energy management routines.

Practice your answers

Technical practice

Translating Ambiguous Supply Chain Business Questions into Analytical Frameworks

What to coverHow do you approach a situation where a supply chain stakeholder presents an ambiguous problem statement regarding inventory or logistics? Guidance: Explain how you collaborate with stakeholders to define operational context, constraints, and success measures, translating vague business goals into clear hypotheses and scoping frameworks.

Choosing Between Descriptive, Predictive, and Prescriptive Modeling Techniques

What to coverWhen would you select an operations research or prescriptive optimization model over a predictive machine learning model for a supply chain problem? Guidance: Discuss the trade-offs between forecasting future outcomes using historical trends versus determining optimal courses of action under operational constraints and capacity limits.

Handling Missing or Noisy Historical Data in Supply Chain Analytics

What to coverWhat techniques do you use for feature engineering and data preparation when dealing with incomplete or noisy historical supply chain datasets? Guidance: Address methods for imputing missing values, detecting anomalies, and validating data readiness before feeding records into machine learning algorithms.

Proficiency in Python and R for Analytics Model Development

What to coverHow do you decide whether to implement a data transformation or modeling pipeline in Python versus R? Guidance: Compare the ecosystem strengths of Python (such as scikit-learn and production integration) and R (such as specialized statistical packages) in the context of scalable analytics.

Writing Intermediate SQL Queries for Large Datasets

What to coverHow would you write an intermediate SQL query to extract and aggregate multi-table shipping and inventory metrics efficiently? Guidance: Focus on utilizing joins, subqueries, and window functions to optimize query performance and ensure clean data extraction for downstream modeling.

Leveraging Power BI and Visualizations for Actionable Insights

What to coverWhat key design principles do you follow when building Power BI dashboards for cross-functional supply chain stakeholders? Guidance: Emphasize clarity, focusing on metrics that drive operational decisions, avoiding clutter, and structuring visual storytelling so non-technical leaders can grasp model recommendations quickly.

Applying DevOps and MLOps Practices to Analytics Solutions

What to coverHow do you ensure that analytical models and applications are sustainable and maintainable post-deployment? Guidance: Discuss incorporating version control using Git, automated testing, containerization, and continuous monitoring practices on cloud platforms like Google Cloud Platform (GCP).

Understanding End-to-End Supply Chain Operations

What to coverHow does an understanding of end-to-end supply chain flows influence your feature selection and model evaluation metrics? Guidance: Explain how variables such as lead times, warehouse capacities, transportation constraints, and demand variability impact model design and business relevance.

Operations Research Techniques in Production and Inventory Planning

What to coverHow can linear programming or simulation techniques be applied to solve inventory optimization or routing challenges? Guidance: Describe how to formulate objective functions, decision variables, and operational constraints to solve complex resource allocation problems.

Behavioural practice

Managing Conflicting Stakeholder Priorities in an Analytics Project

What to coverDescribe a time when supply chain stakeholders had competing expectations regarding project scope or deliverables. Guidance: Use the STAR method to explain the situation, the actions you took to facilitate alignment and assess feasibility, and the final result on project success.

Mentoring and Developing Junior Analysts

What to coverTell me about a time when you coached or mentored a junior analyst to improve their technical or problem-solving skills. Guidance: Detail the situation, your approach to creating individual development plans or training sessions in R/Python, and how their performance or capability improved as a result.

Translating Complex Model Limitations to Non-Technical Audiences

What to coverShare an example of when you had to explain complex model behavior, assumptions, or limitations to business leaders who lacked a data science background. Guidance: Outline the communication strategy you used to clarify technical findings without losing essential nuances, and how stakeholders reacted to the insights.

Working in Extended Shifts and Managing Cross-Functional Collaboration

What to coverHow do you manage your energy, task prioritization, and collaboration when operating in evening shifts or coordinating across distributed teams? Guidance: Provide an example of how you structured your daily schedule, handled urgent dependencies, and maintained high productivity and team engagement.

Sharpen your knowledge

Topics to prepare

End-to-End Supply Chain Operations
Conceptual topic covering supply chain flows, procurement, inventory, and logistics operations. Exercise: Map out a typical manufacturing supply chain lifecycle and identify key bottlenecks where predictive analytics can optimize inventory levels. Relevance: Directly aligns with the job requirement for a strong understanding of end-to-end supply chain operations to translate business context into analytical problems.
Descriptive, Diagnostic, Predictive, and Prescriptive Analytics Framework
Conceptual topic covering the four tiers of data analytics maturity. Exercise: Review a historical demand dataset and design a framework showing how descriptive reporting evolves into prescriptive optimization modeling. Relevance: Core methodology required for designing analytical models across all four quadrants specified in the job description.
Operations Research and Mathematical Optimization
Conceptual topic covering optimization techniques, linear programming, and simulation for decision-making. Exercise: Formulate a basic production-planning optimization problem with capacity constraints and objective functions. Relevance: Explicitly required for developing prescriptive models and operations research solutions mentioned in the responsibilities.

Skills to revise

Python and R Programming for Data Science
Hands-on skill in writing data preparation, feature engineering, and modeling scripts. Exercise: Write a script in Python or R to clean an irregular time-series dataset and build a baseline forecasting model. Relevance: Directly matches the mandatory requirement for proficiency in Python and R for analytics model development and mentoring.
Intermediate SQL and Data Manipulation
Hands-on skill in querying relational databases and aggregating supply chain data. Exercise: Write complex SQL queries utilizing window functions and joins to extract historical shipping and inventory metrics from a relational database. Relevance: Essential for gathering data and supporting the requirement for intermediate SQL proficiency.
Power BI and Data Visualization
Hands-on skill in designing interactive dashboards and translating analytical outputs into actionable insights. Exercise: Create a Power BI dashboard displaying supply chain key performance indicators such as lead time variance and stockout rates. Relevance: Directly ties into the requirement to provide actionable insights through effective presentations and visualizations.
DevOps, MLOps, and GCP Fundamentals
Hands-on skill in deploying and sustaining models using version control and cloud infrastructure. Exercise: Set up a basic CI/CD pipeline using Git and deploy a containerized analytics script to Google Cloud Platform (GCP). Relevance: Aligns with preferred qualifications and responsibilities regarding deploying, documenting, and sustaining models using DevOps and MLOps practices.

Your preparation checklist

Prepare a Supply Chain Advanced Analytics Project Walkthrough
Assemble a structured presentation or code repository from past experience demonstrating end-to-end delivery of descriptive, diagnostic, predictive, or prescriptive models applied to supply chain operations (such as inventory optimization or demand forecasting). Completion looks like having a ready-to-present case study covering business problem translation, methodology choice, and quantifiable impact.
Build and Test R and Python Scripting Portfolios
Review and practice core coding tasks in R and Python, focusing on data preparation, feature engineering, and statistical modeling. Completion looks like having functional, clean script templates for automated testing, data manipulation, and machine learning pipelines stored locally or in version control.
Draft SQL and Data Visualization Artifacts
Refresh intermediate SQL querying skills (joins, window functions, aggregations) and design a sample Power BI dashboard or visualization report. Completion looks like a portfolio sample showing raw data extraction transformed into actionable stakeholder insights.
Formulate Operations Research and Optimization Case Studies
Review foundational concepts in operations research, mathematical programming, and forecasting techniques relevant to supply chain constraints. Completion looks like a documented problem-formulation exercise illustrating linear programming or simulation methodologies.
Prepare Evidence for Stakeholder Management and Mentorship Competencies
Document specific past examples where you led analytics projects, aligned cross-functional stakeholders, and coached junior team members. Completion looks like concise STAR-format summaries highlighting your problem-solving, communication, and leadership capabilities.
Review MLOps, Git, and GCP Deployment Workflows
Familiarize yourself with deployment practices, version control using Git, and cloud infrastructure operations on Google Cloud Platform (GCP). Completion looks like a documented deployment workflow showing how models and applications are sustained and monitored post-launch.
Formulate Thoughtful Questions for the Interviewer
Prepare targeted questions regarding the team's operational rhythm on the 1.30 PM to 10.30 PM shift, collaboration channels with global supply chain segments, and standard model deployment pipelines. Completion looks like a refined list of 4-5 insightful questions demonstrating genuine interest in the role's execution.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…