Data Scientist II - QuantumBlack, AI McKinsey

YOUR IMPACT

Do you want to do work that matters, alongside supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem-solver who is energized by challenges?
You've come to the right place.

You will collaborate with clients and interdisciplinary teams to understand client needs, develop impactful advanced analytics and AI solutions, optimize code, and solve complex business challenges across industries.

You will grow your expertise by contributing to cutting‑edge projects, R&D, and global conferences while working alongside top‑tier talent in a dynamic, innovative environment.

Your work will drive meaningful change. By uncovering patterns in data and delivering innovative solutions, you'll help clients stay competitive, transform operations, and achieve lasting improvements.

• Build a digital twin of a defense supply chain to enhance military hardware availability.
• Leverage agentic AI to improve customer service outcomes for a global travel company.
• Optimize the schedule and funding of a multi‑billion‑dollar capital project to accelerate delivery.

Day‑to‑day, you'll tackle complex challenges in partnership with senior data scientists, engineers, designers, and domain experts.

• Translate business questions into analytical approaches and select the right techniques for each problem.
• Conduct exploratory data analysis.
• Design, implement, and evaluate models — from traditional machine learning to deep learning to LLMs — using rigorous metrics and A/B tests; when appropriate, build production‑grade RAG pipelines and assess LLM output quality/hallucinations.
• Deploy models via APIs or batch pipelines, write unit tests, and set up monitoring dashboards to track performance and drift.
• Document assumptions, communicate results in clear, actionable language, and collaborate with engineers to integrate solutions into user‑facing applications.
• Build models that are accurate, explainable, and free from bias.
• Optimize inference latency and cost through parameter‑efficient tuning, quantization, and accelerated serving stacks.

Additionally, you will contribute to internal tools, participate in R&D projects, and have opportunities to attend and present at leading conferences such as NIPS and ICML.

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 provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible.

• Continuous learning: Our structured apprenticeship culture helps you grow while receiving clear, actionable feedback.
• A voice that matters: From day one, your ideas and contributions shape the work you do.
• Global community: Collaborate with colleagues in 65+ countries.
• World‑class benefits: Competitive salary based on location, experience, and skills, plus a comprehensive benefits package for you and your family.

• Bachelors, Masters or PhD in computer science, machine learning, applied statistics, mathematics, engineering or artificial intelligence.
• 2+ years of professional experience applying machine learning and data mining techniques to real problems with large data sets.
• Programming experience (focus on machine learning): SQL and Python's Data Science stack are a must; knowledge of at least one big data framework (PySpark, Hive, Hadoop) is a plus; R, SPSS, SAS are nice to have;
  Software Engineering experience is a plus.
• Expertise applying machine learning solutions to complex, big‑data problems.
• Ability to prototype statistical analysis and modeling algorithms and apply them for data‑driven solutions in new domains.
• Experience deploying technology applied to business problems is a plus.
• Proficiency with Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, AWS, GCP, Azure.
• Exceptional…