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McKinsey & Company is a global management consulting firm that helps organizations across the private, public, and social sectors achieve lasting success. Founded in 1926, McKinsey has grown into one of the most recognized consulting firms worldwide, with offices in over 65 countries and a network of more than 30,000 professionals.
We work alongside leaders to solve their most pressing challenges, from strategy and operations to digital transformation, sustainability, and organizational design. Our consultants bring deep industry expertise and cutting-edge analytics, enabling clients to make bold decisions, achieve measurable performance improvements, and build capabilities for long-term growth.
McKinsey is committed to making a positive difference not only for our clients but also for society. Through initiatives in sustainability, diversity & inclusion, and social impact, we aim to help shape a more resilient and inclusive global economy.
At our core, we believe in the power of collaboration, innovation, and data-driven insights to transform organizations and unlock human potential.
Data Scientist II – QuantumBlack, AI
McKinsey & Company
On-site
New York, NY, United States
Full-time
$146,600 -
$150,000
About McKinsey & Company
About the Role
As a Data Scientist II, 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.
Qualifications
Bachelor’s degree in computer science with 2+ years of professional experience OR Masters or PhD a discipline such as computer science, mathematics, statistics or electrical engineering.
Development experience (focus on machine learning): SQL and Python’s data-science stack; proficiency with Spark/PySpark for distributed workloads. We use the right tech for the task and often work within clients’ stacks. Technologies you may encounter include Airflow, Databricks, Dask/RAPIDS, containerization with Docker and Kubernetes, and the major clouds (AWS, GCP, Azure, Oracle).
Exceptional time management to meet your responsibilities in a complex and largely autonomous work environment.
Professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data.
GenAI experience a plus: parameter-efficient tuning, RAG architectures, vector-store technologies, LLM evaluation.
Strong communication skills, both verbal and written, in English and local office language(s), with the ability to adjust your style to suit different perspectives and seniority levels.
Willingness to travel.
Development experience (focus on machine learning): SQL and Python’s data-science stack; proficiency with Spark/PySpark for distributed workloads. We use the right tech for the task and often work within clients’ stacks. Technologies you may encounter include Airflow, Databricks, Dask/RAPIDS, containerization with Docker and Kubernetes, and the major clouds (AWS, GCP, Azure, Oracle).
Exceptional time management to meet your responsibilities in a complex and largely autonomous work environment.
Professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data.
GenAI experience a plus: parameter-efficient tuning, RAG architectures, vector-store technologies, LLM evaluation.
Strong communication skills, both verbal and written, in English and local office language(s), with the ability to adjust your style to suit different perspectives and seniority levels.
Willingness to travel.
Responsibilities
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, you’ll 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 which are accurate, explainable, and free from bias.
Optimize inference latency and cost through parameter-efficient tuning, quantization, and accelerated serving stacks.
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, you’ll 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 which are accurate, explainable, and free from bias.
Optimize inference latency and cost through parameter-efficient tuning, quantization, and accelerated serving stacks.
Benefits
Additionally, we provide a comprehensive benefits package that reflects our commitment to the wellness of our colleagues and their families. This includes medical, mental health, dental and vision coverage, telemedicine services, life, accident and disability insurance, parental leave and family planning benefits, caregiving resources, a generous retirement contributions program, financial guidance, and paid time off.
