Director Data Science & Machine Learning

About Us
We are surrounded by the world's leading consumer companies led by technology - Amazon for retail, Airbnb for hospitality, Uber for mobility, Netflix and Spotify for entertainment, etc. Food & Beverage is the only consumer sector where large players are still traditional restaurant companies. At Rebel Foods, we are challenging this status quo as we are building the world's most valuable restaurant company on the internet, superfast.
The opportunity for us is immense due to the exponential growth in the food delivery business worldwide which has helped us build 'The World's Largest Internet Restaurant Company' in the last few years. Rebel Foods' current presence in India & UAE with close to 50 brands and 4500+ internet restaurants has been built on The Rebel Operating System.
While for us it is still Day 1, we know we are in the middle of a revolution towards creating never seen before customer-first experiences. We bring you a once-in-a-lifetime opportunity to disrupt the 500-year-old industry with technology at its core.
We urge you to refer to the below to understand how we are changing the restaurant industry before applying at Rebel Foods.
https://spirit.rebelfoods.com/why-is-rebel-foods-hiring-super-talented-engineers-b88586223ebe
https://spirit.rebelfoods.com/how-to-build-1000-restaurants-in-24-months-the-rebel-method-cb5b0cea4dc8
https://spirit.rebelfoods.com/winning-the-last-frontier-for-consumer-internet-5f2a659c43db
https://spirit.rebelfoods.com/a-unique-take-on-food-tech-dcef8c51ba41
Job Description:
We are looking for an exceptional Director Data Science & Machine Learning to build and lead a world-class Data Science team that drives meaningful business outcomes at scale
.This is not a pure people-management role. This is a high-ownership, player-coach role for someone who can think strategically, execute aggressively, and lead from the front. You will be expected to identify the highest-leverage opportunities across the business, shape the ML roadmap, drive rapid experimentation, and ship production-grade ML systems that materially move core metrics
.You should have a strong track record of building and deploying machine learning systems in production, leading high-performing teams, and solving ambiguous, high-impact business problems. You should be comfortable switching between leadership, architecture, hands-on debugging, stakeholder alignment, and execution review — sometimes on the same day
.If you’re excited by messy real-world problems, high scale, fast decisions, and building with intensity, this role is for you.
What our team owns
At Rebel, the Data Science & Analytics team owns the intelligence layer for the company.
We build and maintain the AI based solutions, ML models, optimization systems, rule engines, and decision frameworks that power critical business outcomes across the organization. This includes:
Demand and inventory forecasting
Order volume prediction
Marketing spend optimization and attribution
Delivery time prediction and logistics intelligence
Personalization and recommendation systems
Capacity planning and operational optimization
Pricing and assortment intelligence
Automated business reporting and decision support
We work across nearly every major business function — Supply, Operations, Demand Generation, D2C, Finance, Brands, Customer Delight, Central Planning, and more.
The problems are real, large-scale, cross-functional, and deeply operational. The impact is visible. The feedback loops are fast. The opportunity to create leverage is massive.
We’re looking for someone who can
Set the bar for what great looks like in applied Data Science and ML execution across the organization.
Define and drive the Data Science roadmap by identifying the highest-impact problems worth solving, not just the loudest asks.
Lead, hire, and grow a high-caliber team of Data Scientists / ML Engineers with a culture of ownership, urgency, and technical excellence.
Own end-to-end delivery of ML initiatives — from business framing and experimentation to deployment, monitoring, and continuous improvement.
Act as a player-coach: unblock teams, review modeling approaches, challenge assumptions, and go hands-on when the situation demands it.
Partner deeply with Product, Engineering, and Business leaders to align on priorities, define success metrics, and drive adoption of solutions.
Build production-grade, scalable, and reusable ML systems with strong engineering rigor and long-term maintainability.
Drive a high-velocity experimentation culture with clear hypotheses, measurable outcomes, and ruthless focus on business impact.
Establish best practices across the ML lifecycle — feature engineering, validation, deployment, monitoring, retraining, governance, and documentation.
Push the team beyond dashboards and incremental models into decision automation, optimization, and durable intelligence systems.
Communicate clearly and influence decisively, especially in ambiguous or high-stakes cross-functional situations.
Raise the organization’s ML maturity by improving trust, adoption, and understanding of Data Science across teams.
Specific Qualifications
9+ years of experience in Data Science / Machine Learning, with 2+ years of leading teams or managing high-performing DS/ML groups.
Proven track record of shipping ML systems to production that delivered measurable business impact at scale.
Strong experience in applied machine learning, statistical modeling, and optimization, especially in fast-moving business environments.
Deep hands-on expertise with ML frameworks and libraries such as PyTorch, TensorFlow/Keras, Scikit-learn.
Strong understanding of core ML methods including:
Regression
Classification
Tree-based / Gradient Boosting models
Time-Series Forecasting
Ranking / Recommendation systems
NLP
Optimization and decision systems
Strong proficiency in Python, SQL, Pandas, NumPy, and data analysis / visualization libraries such as Matplotlib, Plotly, etc.
Experience building robust ML workflows including:
Feature engineering pipelines
Offline / online evaluation
Experiment design and A/B testing
Model deployment and serving
Monitoring and retraining strategies
Strong understanding of MLOps and production engineering, including:
Dockerization
REST APIs
CI/CD for ML workflows
Version control and reproducibility
Scalable inference and model lifecycle management
Experience with optimization frameworks (e.g. Google OR-Tools) is a strong plus.
Strong intuition for balancing speed vs rigor, and the judgment to know when each matters.
Excellent communication and stakeholder management skills, with the ability to challenge, influence, and align senior cross-functional leaders.
Good-to-have
Experience in Time-Series Forecasting, Market Mix Modeling (MMM), Causal Inference, or Experimentation Platforms.
Experience in personalization, recommendation systems, pricing, logistics, supply chain, or operational optimization.
Experience building ML platforms, reusable modeling frameworks, or internal DS productivity tooling.
Prior experience in consumer internet, e-commerce, food-tech, logistics, mobility, or other high-scale operational businesses.
Strong bias toward building systems that create compounding leverage, not one-off analyses.
Passion for raising the technical bar and building teams that attract top-tier talent.
If you find this intriguing, we should meet.
Please reach out to kavya.guwalani@rebelfoods.com with your background and a few lines on why you want to help build the future of internet restaurants.
Learn more about Rebel
Culture @ Rebel

Work arrangement
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