As a data science consultant at QuantumBlack, AI by McKinsey, you will work in multi-disciplinary environments harnessing data to provide real-world impact for organizations globally.
You will influence many of the recommendations our clients need to positively change their businesses and enhance performance.
In this role, you will be responsible for working on complex and extremely varied data sets from some of the world’s largest organizations to solve real-world problems. You will develop high-quality data science products and solutions for clients as well as assets for our internal data teams, focusing on modeling. Additionally, you will present findings, recommendations, and provide consultation to stakeholders, both internal and clients.
Throughout your tenure, you will learn to work across various industries and sectors, gaining insights into diverse business challenges, applications of data science and AI engineering. You will build large-scale data and analytics solutions, handling complex problems and advanced client situations. You will also learn best practices in data and analytics development, including framework, responsibility model, high-quality code, data security, sustainability, and scalability. Working in collaborative teams with diverse skill sets, you will foster effective teamwork and learn from colleagues in a multicultural and creative environment. Staying up-to-date with the latest advancements in generative and agentic AI, data science tools, techniques, and technologies, as well as participating in first-class learning programs, will be a key part of your development. You will also develop strong client-facing skills, including effective communication, problem-solving, and presentation abilities.
You will work with other data scientists, data and software engineers, designers, project managers, and business subject matter experts on interdisciplinary projects across various industry sectors to enable business ambitions with data and analytics.
Our tech stack is diverse. While we advocate for using the right tools for the right task, we also take into account the client’s current landscape and preferences. Often, we use Python, PySpark, Databricks, SQL, Docker, and Kubernetes. We work on a regular basis with cloud service providers such as AWS, GCP, and Azure.
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