Data Scientist II – QuantumBlack, AI by McKinsey Join to apply for the Data Scientist II – QuantumBlack, AI by McKinsey role at QuantumBlack, AI by McKinsey. 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. Benefits

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. 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. 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. 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.

Your Impact As a Data Scientist II, you will collaborate with clients and interdisciplinary teams to develop advanced analytics solutions, optimize code, and solve complex business challenges across industries. You’ll translate business challenges into analytical problems, build models to solve them, and ensure they are evaluated with relevant metrics. You’ll contribute to internal tools, participate in R&D projects, and have opportunities to attend and present at leading conferences. Qualifications

Master’s or PhD in Computer Science, Machine Learning, Applied Statistics, Mathematics, Engineering, Physics, or other technical fields. 2–5+ years of professional experience applying machine learning and data mining techniques to solve real‑world problems with substantial data sets. Programming experience: SQL and Python’s Data Science stack; knowledge of at least one big data framework (e.g., PySpark, Hive, Hadoop) is a plus. Strong understanding of machine learning methods and experience applying them to complex, data‑rich environments. Ability to prototype and deploy statistical and machine learning algorithms, and translate analytical outputs into data‑driven solutions. Experience deploying ML/AI technologies into production or applied business environments is a plus. Excellent time management skills to handle responsibilities in a complex and largely autonomous environment. Willingness to travel. Strong communication skills, both verbal and written, in English.

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