Data Science Manager; d/f/m
Verfasst am 2026-02-06
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IT/Informationstechnik
Künstliche Intelligenz Ingenieur, Maschinelles Lernen, Data Science Manager, Datenwissenschaftler
We’re looking for a Data Science Manager (d/f/m) to lead our central Data Science team!
Our mission is to deliver data-driven insights and machine learning (ML), including state-of-the-art Generative AI (GenAI) solutions, that enhance experiences and drive value across our platforms. We work collaboratively with product teams, leveraging cutting-edge technologies to tackle a wide range of challenges and opportunities.
The role:
As a Data Science Manager, you will split your time between managing and developing a high-performing Data Science team and driving impactful projects as an individual contributor. You’ll play a key role in shaping our strategy, ensuring project delivery, and enabling the growth and success of both your team and our products.
Team Leadership:
Build, mentor, and lead a multidisciplinary data science team, encouraging upskilling and experimentation in GenAI, cloud computing (GCP, AWS), and modern Data Science tools.Strategic Direction:
Collaborate with product and engineering leadership to define the data science strategy, with a focus on integrating GenAI technologies (e.g., Gemini, AWS Bedrock, LLMs) and cloud-native solutions into our business.Project Oversight:
Oversee pipelines, prioritize GenAI and cloud-based initiatives, and ensure effective resource allocation for AI-driven innovation on GCP and AWS infrastructure.Stakeholder Management:
Educate and guide stakeholders on innovative solutions using GCP, AWS, Gemini, and Bedrock.Performance Management:
Foster development through coaching in cloud platforms and state-of-the-art Generative AI.
Technical Leadership:
Lead by example in developing and deploying advanced machine learning and GenAI models (e.g., LLMs, Gemini), using cloud-native tools (GCP Vertex AI, AWS Sagemaker/Bedrock) and scalable databases like Big Query.GenAI Implementation:
Drive hands-on prototyping and productionization of GenAI use cases, leveraging technologies such as Gemini (GCP) and Bedrock (AWS) for tasks including personalized recommendations, content generation, and automation workflows.Project Delivery:
Own end-to-end delivery of data science and GenAI projects using tools like Big Query, Vertex AI, Gemini, and cloud-based ML pipelines.Innovation:
Stay on top of developments in GenAI, transformers, and cloud offerings, integrating technologies like Big Query for big data analysis and Gemini/Bedrock for GenAI applications.
MSc or Ph.D. in a quantitative field (statistics, computer science, economics, or related).
5+ years of professional experience in Data Science, with hands‑on technical roles and 1‑2 years in a leadership or mentorship capacity.
Proven track record delivering ML/GenAI solutions into production, preferably in marketplaces or tech industries.
Advanced experience with Python and ML frameworks.
Practical experience with GenAI technologies:
- Large Language Models (e.g., Gemini, GPT, PaLM, Llama, and open‑source alternatives)
- Amazon Bedrock for foundation model deployment
- Integration with GCP Vertex AI and Big Query, AWS Sagemaker and Bedrock
Strong background in traditional Machine Learning, NLP techniques, and MLOps best practices for production deployment in cloud environments.
Proficient with SQL and No
SQL databases, with emphasis on Big Query for scalable analytics.Experience managing and coaching technical teams.
Track record collaborating with cross‑functional product development teams.
Passion for agile (Kanban/Scrum) software development, product discovery, and continuous deployment.
Excellent English communication skills, both written and verbal.
Selected Technologies & Tools:
- Cloud Platforms: GCP (Vertex AI, Big Query, Gemini), AWS (Sagemaker, Bedrock)
- Programming:
Python, R, SQL, No
SQL - ML Frameworks:
Tensor Flow, PyTorch, Hugging Face, Lang Chain - GenAI: LLMs (Gemini, GPT, Llama), diffusion/creative models, prompt engineering
- MLOps: ML pipelines, cloud-based deployment
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