Data Scientist, Adevinta

• 6+ years of experience as a Data Scientist, with a strong focus on deep learning and NLP. Computer vision is a plus
• Advanced proficiency in Python, including experience with PyTorch or TensorFlow
• Expertise in designing, training, and deploying complex ML models, particularly in multimodal learning environments
• Strong background in statistical analysis and experimental design
• Proficiency in data manipulation and analysis using SQL, Spark, and pandas
• Experience with cloud-based ML infrastructure (e.g. AWS)
• Familiarity with containerization and orchestration tools (Docker, Kubernetes)
• Solid understanding of MLOps principles and experience with CI/CD for ML workflows
• Programming Languages: Python (advanced), SQL
• ML Frameworks: PyTorch or TensorFlow, Hugging Face Transformers
• Data Processing: Spark, pandas, NumPy
• MLOps Tools: MLflow, Kubeflow, or equivalent
• Visualization: Matplotlib, Seaborn, Plotly
• Version Control: Git, DVC
• Cloud Platforms: AWS
• Containerization: Docker, Kubernetes

Desirable

• We're seeking an experienced Data Scientist to join our innovative team. You'll apply advanced machine learning techniques, statistical modeling, and deep learning algorithms to develop groundbreaking solutions for our business challenges
• You’ll play a crucial role in the design and architecture effort to ensure successful project completion. You’ll maintain and improve the quality of the codebase
• Design, implement, test, and deploy end-to-end Machine Learning models, with a focus on multimodal learning and Vision Language Models (VLMs)
• Advance our ML practices by exploring novel architectures, implementing transfer learning techniques, and optimizing model performance
• Contribute to and use robust MLOps practices, including model versioning, experiment tracking, and automated deployment pipelines with the engineers
• Set and use observability systems for LLMs, including performance monitoring, bias detection, and drift analysis with the team
• Conduct rigorous A/B testing and statistical analysis to validate model improvements with the product
• Build data pipelines and feature engineering processes to enhance model efficiency and accuracy, partnering with our machine learning engineer
• Attend international conferences on the domain and bring back that knowledge to the company
• Write documentation and publication (paper, speech, blog post) about our work