Responsibilities
- Shape the strategic direction and vision of Data Science across the organization by identifying transformative AI/ML opportunities and championing the team’s evolving role in a rapidly advancing GenAI landscape.
- Lead the design and deployment of predictive and generative AI models that power personalization, pricing, search, and optimization in marketplace and fintech domains.
- Collaborate with product leaders to align ML initiatives with strategic business goals and drive product innovation through data-driven experimentation and modeling.
- Architect scalable ML infrastructure and automated workflows using cloud-native tools (e.g., AWS, EC2, Kubernetes) to support efficient model training, deployment, and analytics across diverse datasets.
- Ensure long-term performance and compliance of production AI models though robust governance and monitoring.
- Provide technical leadership and mentorship to data scientists, shaping an innovative team culture and fostering high-performance and continuous learning.
- Serve as a cross-functional technical leader, shaping company-wide technical initiatives beyond data science. Partner with engineering, product, and platform teams to influence architecture, innovation agendas, and technical standards across the organization.
- Act as a senior technical advisor, leading resolution of complex modeling issues and acting as the escalation point for critical incidents.
- Lead the exploration and strategic application of GenAI, identifying high-impact use cases and guiding their integration across products and platforms.
Requirements
- Advanced academic credentials in a quantitative field such as Computer Science, Engineering, Mathematics, or related discipline
- 10+ years of experience in data science, machine learning, or applied AI, with a strong portfolio of high-impact projects in production
- Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
- Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods
- Deep knowledge of machine learning algorithms (supervised, unsupervised, deep learning), including model evaluation, explainability, and selection for business-critical use cases
- Strong hands-on experience with cloud infrastructure (AWS), containerization (Docker), and orchestration (Jenkins, Airflow)
- Proven capability in MLOps, including CI/CD pipelines, model monitoring, versioning, and automated retraining
- Experience deploying and serving models through APIs (e.g., Flask, FastAPI) in both real-time and batch-processing environments
- Excellent communication and stakeholder management skills, able to translate complex concepts into actionable insights for non-technical audiences
- Demonstrated success mentoring teams, guiding technical strategy, and advocating for best practices in experimentation, reproducibility, and ethical AI
- Experience working in agile product development environments (Scrum/Kanban); experience influencing product roadmaps is a strong plus
Benefits
- A team and company environment that gives you lots of opportunities to innovate and shape our business and culture.
- A highly competitive compensation package and a range of personal benefits such as discounts from our partners
- Targeted trainings, workshops, coaching, and support services that help you grow at AutoScout24 – and in life
- Cutting-edge laptops (Apple or Windows) and other personal tech equipment that you can also use privately; our super-friendly Employee Tech team will ensure that your tech needs are always taken care of
- Modern, cloud-native tooling and infrastructure (including AWS, Docker, and GitHub Actions) that enable fast, scalable experimentation and deployment
- JobRad and Urban Sports Club
- Regular virtual and in-person team and company events to have fun, share, and celebrate successes (including hackathons, Christmas parties, and the Oktoberfest)
- 30 vacation days a year, flexible working hours, and hybrid work
- A dog-friendly office; we love our four-legged friends, so feel free to bring yours with you
Additional Information
- 30 vacation days a year
- Flexible working hours
- Hybrid work model (2 days a week in office)
- Dog-friendly office
- Use of personal tech equipment for private use
- Modern, cloud-native tooling and infrastructure (AWS, Docker, GitHub Actions)
