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McKinsey & Company · 4 months ago

Data Scientist II – QuantumBlack, AI by McKinsey (Critical Industries)

New Jersey, United States

Full-time

Onsite

Entry, Mid Level

$147K/yr - $150K/yr

2+ years exp

McKinsey & Company is a global management consulting firm that addresses complex challenges across various industries. As a Data Scientist II, you will collaborate with clients and interdisciplinary teams to develop advanced analytics and AI solutions, driving meaningful change by uncovering patterns in data and delivering innovative solutions.

ConsultingManagement ConsultingProfessional Services

Growth Opportunities

No H1B

U.S. Citizen Only

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

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

Qualification

Machine LearningData MiningPythonSQLSpark/PySparkGenAITime ManagementCommunication Skills

Required

U.S. Citizenship is required (this role must be able to be staffed on Critical Industries work which includes Defense, Aerospace, Utilities, etc.)

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

Professional experience in applying machine learning and data mining techniques to real problems with copious amounts of data

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

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