Data Scientist - Customer One
McKinsey & Company · San Jose, Costa Rica · 1d ago
Mid levelOn-siteData & Analytics
Who you'll work with
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:
- Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
- A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
- Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
- World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.
What you'll do
You will design, build, and deploy robust, scalable machine learning models tailored specifically for customer value management and personalization applications.
As part of this process, you'll adapt and fine-tune these models to guarantee they deliver significant business value while meeting client-specific needs. You will develop new algorithms that tackle emerging business challenges, which include curating, wrangling, and preparing the high-quality data necessary for these advanced analytics. To ensure optimal performance, you will routinely perform hyperparameter tuning, model validation, and continuous efficiency optimization.
Collaboration is a key part of this position, so you'll work closely alongside cross-functional teams—including Data Engineers, Product Managers, and industry specialists—to seamlessly integrate your models into client solutions and drive impactful results. You will take full ownership of your technical work streams, championing best practices in model development and deployment from start to finish.
Additionally, your expertise will help advance our CustomerOne platform by integrating cutting-edge technologies and methodologies, such as Generative AI. Beyond the technical build, you will be a vital partner to our internal users and clients, actively participating in joint problem-solving sessions and guiding them on how to best leverage these machine learning models to achieve their business objectives.
Your background
- Degree in Data Science, Computer Science, Statistics, or equivalent experience preferred
- Experience with cloud platforms (e.g. AWS, Azure, GCP) and Databricks a plus
- Experience with Generative AI technologies, including interfacing with foundational models (e.g., OpenAI GPT-4), designing AI Agents, and prompt engineering for various content creation tasks
- Familiarity with distributed computing frameworks (e.g., Spark, ibis), containerization (e.g., Docker), and analytics libraries (e.g. pandas, numpy, scikit-learn)
- Proven experience in building and deploying machine learning models for advanced analytics use cases
- Proficiency in writing clean, maintainable, scalable, and robust code in Python in a professional setting
- Practical knowledge of data science concepts and best practices, including feature engineering, model validation, and hyperparameter tuning