Data Scientist - QuantumBlack, AI by McKinsey

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.

Who You Are

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.

Our Tech Stack

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.

Your Impact

As a Data Scientist at QuantumBlack, 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.

Role Responsibilities

• Work on complex and extremely varied data sets from some of the world’s largest organizations to solve real world problems
• Develop high quality data science products and solutions for clients as well as assets for our internal data teams
• Focus on modelling, working alongside the Data Engineering team
• Present findings, recommendations and provide consultation to stakeholders (internal and clients)
  
  

What You’ll Learn

• Work across various industries and sectors, gaining insights into diverse business challenges and applications of data science
• Build large-scale data & analytics solutions, handling complex problems and advanced client situations
• Best practices in data & analytics development including framework, responsibility model, high quality code, data security, sustainability and scalability
• Work in collaborative teams with diverse skill sets, fostering effective teamwork and learning from colleagues in a multicultural and creative environment
• Stay up-to-date with the latest advancements in data science tools, techniques, and technologies as well as first class learning programs
• Develop strong client-facing skills, including effective communication, problem-solving, and presentation abilities
  
  

Your Qualifications and Skills

• Degree in (business) maths, statistics, machine learning, data science, physics, computer science or engineering required
• Master's degree or PhD level in the field of data science, computer science, business informatics or equivalent is a plus
• Up to 2 years of professional hands-on experience in applying advanced data science methods and implementing technical data solutions (e.g. develop machine learning models), in order to solve business problems
• A strong grasp of various analytics landscape, including business intelligence, machine learning, AI, and generative AI, and how to apply them effectively in different scenarios
• Proficiency in Python, SQL (focus on data & analytics applications)
• Professional experience in cloud-based analytical tools from market-leading providers such as AWS, MS Azure, Google Cloud
• Proficient communication skills in German and English
• Effective communication and presentation skills, particularly the ability to explain complex analytical concepts in a comprehensible manner adapted to different groups of non-technical audiences e.g. business managers, heads of products, sales & marketing leads
• Practical experience in Generative AI application development
• Proven record of advisory works
• Proven record of leadership in a work setting and/or through extracurricular activities