QUALIFICATIONS
- Bachelor's or master's level in a discipline such as: computer science, machine learning, applied statistics, mathematics, engineering or artificial intelligence
- 2+ years of deep technical experience in machine learning, advanced analytics and statistics
- Advanced programming expertise in Python
- Proven application of advanced analytical, data science and statistical methods in realworld engagements
- Good presentation and communication skills, with a knack for explaining complex analytical concepts and insights to technical as well as non-technical audiences
- Knowledge in Engineering standards, QA/Risk Management
- Experience and expertise In GenAI application development (RAG, Agentic flows, etc.) using API integration and orchestration tools such as Langchains, Crew.AI, AutoGen will be an added advantage
- Willingness to travel both domestic and international
WHO YOU'LL WORK WITH
You will work with other data scientists, data/ML engineers, designers, project managers and business subject matter experts on interdisciplinary projects across various industry sectors to enable business ambitions with data & analytics.
You are a highly collaborative individual who is capable of laying aside your own agenda, listening to and learning from colleagues, challenging thoughtfully and prioritizing impact. You search for ways to improve things and work collaboratively with colleagues. You believe in iterative change, experimenting with new approaches, learning and improving to move forward quickly.
While we advocate for using the right tools for the right task, we also take into account client’s current landscape and preferences. Often, we use Python, PySpark, TensorFlow, PyTorch, Databricks, SQL, Docker and Kubernetes. We also leverage our own proprietary tools such as Kedro, CuasalNex, MLRun (check out more OSS here: https://www.mckinsey.com/capabilities/quantumblack/labs). We work on regular basis with cloud service providers such as AWS, GCP and Azure.
As a Data Scientist, you will:
- Solve the hardest business problems with our clients in multiple industries worldwide while leading research and development of state-of-the-art Machine Learning and statistical methods
- Play a leading role in bringing the latest advances in AI and deep learning to our clients, collaborating with industry executives and QuantumBlack experts to find and execute opportunities to improve business performance using data and advanced machine learning models
- Identify machine learning R&D initiatives that have high potential of applicability in industry
- Work with QuantumBlack leadership and client executives to understand business problems and map them to state-of-the-art analytics and AI solutions
- Work closely with other data scientists, data engineers, machine learning engineers and designers to build end-to-end analytics solutions for our clients that drive real impact in the real world
- Perhaps most importantly, you will work in one of the most talented and diverse data science teams in the world
WHAT YOU'LL DO
At McKinsey you will work on real-world, high-impact projects across a variety of industries. You will have the opportunity to collaborate with QB/Labs teams and build complex and innovative ML systems to accelerate our work in AI and help solve business problems at speed and scale.
You will experience the best environment to grow as a technologist and a leader. You will develop a sought-after perspective connecting technology and business value by working on real-life problems across a variety of industries and technical challenges to serve our clients on their changing needs.
You will be surrounded by inspiring individuals as part of diverse and multidisciplinary teams. You will develop a holistic perspective of AI by partnering with the best design, technical, and business talent in the world as your team members.
While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, Azure, and more.
