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McKinsey Gurugram is seeking an experienced ML/Data Engineer to design, build, and deploy robust machine learning models for customer value management and personalization. You will adapt models for client needs and optimize performance while maintaining data quality and governance across cloud deployments.
The role requires 6+ years of data engineering experience, strong Python coding skills, and familiarity with PySpark, Docker, Databricks, and AI toolchains.
Job Description:
Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
When you join us, you will have:
You will design, build, and deploy robust, modular, scalable, and reproducible machine learning models tailored to various customer value management and personalization applications and you will adapt and fine-tune models to ensure they deliver significant business value and meet client-specific needs.
You will develop new machine learning models and algorithms to address emerging business challenges and opportunities in customer value management. You will also perform hyperparameter tuning, model validation, and performance optimization to enhance model accuracy and efficiency.
Lastly, you will curate, wrangle, and prepare data for use in advanced analytics models, ensuring high-quality input features.
Our current tech stack includes Python, ibis, PySpark, the and our open-source data pipelining framework, Kedro. We support deployment via Docker, Databricks, and cloud platforms such as AWS, GCP, and Azure. Our CI/CD processes utilize Github Actions, and we manage package publishing with JFrog Artifactory and internal tools. We are also exploring the integration of GenAI tools to enhance our capabilities (e.g., for automated deployment).
Requirements: