Data Science Team Lead

2 days ago


Stockholm, Stockholm, Sweden EQT Group Full time

EQT Group is committed to attracting top talent representing diversity of backgrounds, experiences, perspectives, and opinions as this is vital to building better businesses, engaging talent, and driving high performance and financial returns.

As a full-time Staff Machine Learning Engineer based in Stockholm, you'll join our Platform team, engaging with modern technologies and highly skilled colleagues. Your technical expertise and ability to solve complex challenges make you a driving force in engineering excellence.

Key Responsibilities
  • Bridging between Machine Learning/AI research and operations, ensuring that AI-powered solutions are not only accurate during development but also robust and scalable in real-world production scenarios.
  • Designing, implementing, deploying, and managing AI-powered solutions delivering on organizational goals and industry standards, evolving and maintaining the Motherbrain AI platform.
About the Role

You will be working together with Data Scientists, Fullstack Engineers, Backend Engineers, Data Engineers, UI and UX Designers, and Business Stakeholders to take the Motherbrain platform to the next level and leverage AI as a core part of EQT's strategy to maximize its competitive advantage.

Required Skills and Qualifications
  • Technical Leadership: Experience in teams working with disruptive technologies with high level of interaction and collaboration, evolving and maintaining the technical product roadmap according to the business needs.
  • Ai/ML Systems Architecture: Envisioning, operationalizing, building, and deploying end-to-end ML and AI pipelines at scale.
  • Efficient Text Data Processing: Designing and optimizing systems for processing large volumes of textual data efficiently.
  • ML Workflows and Pipelines Orchestration: Building and maintaining MLOps pipelines, including CI/CD for machine learning workflows.
  • Ai Cloud Infrastructure: Working with cloud platforms such as AWS, GCP, or Azure for ML infrastructure.


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