Who You'll Work With You will be part of our global Data Science community, collaborating with data scientists, data engineers, machine learning engineers, designers, and project managers on interdisciplinary projects.
Your work will involve applying math, stats, and machine learning to extract insights from raw data across various industry sectors.
You are a highly collaborative individual who can set aside personal agendas, listen and learn from colleagues, challenge thoughtfully, and prioritize impact.
You seek to improve processes, work collaboratively, and believe in iterative change, experimenting with new approaches, learning, and progressing quickly.
Our Tech Stack While we advocate for choosing the right technology for each task, our common tools include Python, PySpark, the PyData stack, SQL, Airflow, Databricks, our open-source data pipelining framework Kedro, Dask/RAPIDS, container technologies like Docker and Kubernetes, and cloud solutions such as AWS, GCP, and Azure.
Your Impact At McKinsey, you'll work on real-world, high-impact projects across industries, identifying micro-patterns in data that clients can leverage for competitive advantage.
Your solutions will transform their day-to-day business operations.
You'll grow as a technologist and leader by tackling real-life problems, connecting technology with business value, and working with diverse, multidisciplinary teams.
You'll develop a holistic AI perspective by collaborating with top design, technical, and business talent.
As a Data Scientist, you will: Partner with clients, from data owners and users to C-level executives, to understand their needs and build impactful analytics solutions.
Contribute to problem-solving sessions and deliver presentations to colleagues and clients.
Translate business problems into analytical challenges and develop models evaluated with relevant metrics.
Write optimized code to enhance our internal Data Science Toolbox.
Engage in research and development, presenting at conferences like NIPS and ICML, and sharing knowledge within the team.
Work within one of the most advanced data science teams globally.
Develop frameworks and libraries used by data scientists and engineers to move from data to impact.
Guide global companies through data science solutions to transform and improve performance across various industries, including healthcare, automotive, energy, and sports.
Your Qualifications and Skills Bachelor's, Master's, or PhD in computer science, machine learning, applied statistics, mathematics, engineering, or AI.
2-5 years of professional experience applying machine learning and data mining to real problems with large datasets.
Programming experience focusing on machine learning, with proficiency in SQL and Python's Data Science stack.
Knowledge of big data frameworks like PySpark, Hive, or Hadoop is a plus.
Experience with R, SPSS, SAS, or software engineering is beneficial.
Ability to prototype statistical analyses and models, applying them to data-driven solutions in new domains.
Experience deploying technology solutions for business problems is advantageous.
Knowledge of applying machine learning to complex and large datasets.
Willingness to travel.
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