WHAT MAKES US A GREAT PLACE TO WORK

We are proud to be consistently recognized as one of the world’s best places to work. We are currently the #1 ranked consulting firm on Glassdoor’s Best Places to Work list and have maintained a spot in the top four since the list began in 2009.
Extraordinary teams are at the heart of our business strategy—but they don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive—professionally and personally.

REQUIRED QUALIFICATIONS

• Advanced degree in Computer Science, Engineering, Statistics, or related field
• Minimum 5 years of experience in data science or advanced analytics, with at least 3 years in applied analytics roles
• Proficiency in Python, with solid experience in object-oriented programming, testing, and scalable ML workflows
• Experience deploying models via Docker and orchestrating using tools such as Kubernetes
• Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins)
• Experience with MLOps tools (e.g., MLflow, Kubeflow) and orchestration engines (e.g., Airflow, Luigi)
• Comfortable working with both SQL and NoSQL databases
• Strong command of cloud platforms (AWS, Azure, GCP) and infrastructure for ML at scale
• Deep understanding of machine learning methods (supervised, unsupervised) and at least one specialized domain (e.g., NLP, computer vision, reinforcement learning)
• Excellent communication skills and ability to collaborate across technical and non-technical teams
• Fluency in English and German (professional level)

• Serve as the data science lead on cross-functional client teams, developing and deploying enterprise-grade ML solutions
• Build scalable models, algorithms, and tools to solve complex business problems and foster innovation
• Translate high-level business questions into structured, actionable data science projects
• Partner with consulting and engineering teams to operationalize models and ensure robust deployment environments
• Lead data transformation efforts, including structuring, cleaning, and enriching diverse datasets
• Establish best practices for model development, documentation, and reuse across Bain’s analytics platforms
• Provide technical mentorship and communicate technical concepts clearly to non-technical stakeholders
• Stay current with emerging data science technologies and identify opportunities to expand our capabilities and impact
• Travel to the client side will be required