Data Science Manager (d/f/m)

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.

Managerial Responsibilities (50%):

• 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.

Individual Contributor Responsibilities (50%):

• 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 BigQuery.

• 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 BigQuery, Vertex AI, Gemini, and cloud-based ML pipelines.

• Innovation: Stay on top of developments in GenAI, transformers, and cloud offerings, integrating technologies like BigQuery for big data analysis and Gemini/Bedrock for GenAI applications.

Your profile:

• 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 BigQuery, 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 NoSQL databases, with emphasis on BigQuery 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, BigQuery, Gemini), AWS (Sagemaker, Bedrock)

• Programming: Python, R, SQL, NoSQL

• ML Frameworks: TensorFlow, PyTorch, HuggingFace, LangChain

• GenAI: LLMs (Gemini, GPT, Llama), diffusion/creative models, prompt engineering

• MLOps: ML pipelines, cloud-based deployment