Open in all locations: France, Germany, the Netherlands, and the United Kingdom
We are seeking a Lead Data Scientist to spearhead the development, delivery, and execution of simple data as well as complex ML/AI solutions. This role will involve driving the entire lifecycle of ML/AI data products, including product discovery, development, and deployment for both customer-facing and internal applications.
Team & Technology
- Data Products Team:
The Data Products Team is responsible for developing customer-facing data products that leverage advanced analytics and ML/AI. Their work focuses on creating solutions like recommendation engines, ranking algorithms, and predictive models that enhance user experience and drive business growth.
- Data Science Business Enablement:
The Data Science Business Enablement team focuses on building internal data products using advanced analytics and ML/AI. Their role is to develop tools and platforms that support internal business processes, such as experimentation frameworks, document validation systems, and applications of LLMs, enabling more informed decision-making and operational efficiency within the organisation.
- In our technology stack, you will find:
- AWS services like SageMaker, ECR, Lambda, API Gateway, DynamoDB, RDS, S3, EC2, CloudWatch
- Python
- SQL
- ML frameworks including TensorFlow, PyTorch, and Scikit-learn
- Docker
- MLOps tools like MLflow, Kubeflow, Terraform, Graphana, Sagemaker Experiments
- Git
- Orchestration tools like Airflow, Sagemaker Pipelines, and AWS EventBridge
Your role includes the following
1. Product Discovery and Strategy:
2. Development and Execution:
Design, build, and refine ML/AI solutions, such as recommendation engines and LLM tools.
Work hands-on to translate business needs into clear technical requirements.
3. Product Delivery:
Lead and contribute to the full lifecycle of data products, from ideation to deployment.
Implement best practices for scalable, reliable solutions and continuous improvement.
4. Collaboration and Communication:
Serve as the key liaison between data science and other teams, ensuring alignment and clear communication.
Communicate product value and technical details to drive adoption.
5. Leadership and Mentorship:
Lead by example with hands-on involvement in ML/AI development while mentoring the team.
Encourage continuous learning and innovation, applying the latest data science advancements.