Key skills
AWSAzureCloudGoogle Cloud PlatformNumpyPandasPythonScikit-LearnSQLAIMachine LearningGenerative AIscikit-learnNumPyMLOpsMLflowKubeflowGCPGoogle CloudSageMakerCommunicationSales
About this role
Role Overview
- Drive the entire machine learning lifecycle, from exploratory data analysis (EDA) and advanced feature engineering to model training, validation, deployment, and post-launch monitoring for performance and concept drift
- Translate ambiguous business requirements and domain challenges into well-defined technical problems, testable hypotheses, and robust machine learning solutions
- Design, test, and validate multiple modeling approaches to find the optimal solution, establishing clear and relevant evaluation metrics that directly align with business goals
- Utilize our Triple AI SageMaker environment to efficiently train, deploy, and manage scalable models in a production setting
- Communicate complex model outputs and data-driven insights through compelling storytelling and clear visualizations
- Develop a deep understanding of the business domain and product vision
- Actively collaborate with engineers, product managers, and business leaders
- Proactively identify opportunities for impact and focus on delivering concrete business results and outcomes over exhaustive documentation
Requirements
- Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- 3+ years of hands-on professional experience in a data science role focused on building and deploying machine learning models
- Strong proficiency in Python and its core data science libraries (e.g., pandas, NumPy, scikit-learn, Matplotlib/Seaborn)
- Solid proficiency in SQL for complex data querying, transformation, and analysis
- Experience building models for business applications such as forecasting, classification, clustering, or regression
- Familiarity with at least one major cloud platform (AWS, GCP, Azure)
- Preferred Qualifications: 5+ years of experience in a product-focused data science environment
- Ideally, direct hands-on experience using Amazon SageMaker for model development, training, and deployment
- Proven experience implementing and managing model monitoring systems to detect data and model drift in a production environment is highly desired
- A forward-looking interest in the application of Generative AI, with an enthusiasm to learn how to combine LLMs and other generative techniques with traditional machine learning
- Hands-on experience with MLOps principles and tools (e.g., MLflow, Kubeflow, feature stores)
- Exceptional communication and data storytelling skills, with a proven ability to listen, understand business context, and influence both technical and non-technical audiences
- A strong portfolio of completed data science projects that demonstrates a focus on delivering business impact.
Tech Stack
- AWS
- Azure
- Cloud
- Google Cloud Platform
- Numpy
- Pandas
- Python
- Scikit-Learn
- SQL
Benefits
- Annual bonus payment based on your performance (target 20%)
- Dedicated training budget (training, certifications, conferences, diversified career paths etc.)
- Recharge Fridays (2 Fridays off per quarter available)
- Take time Program (up to 3 months of leave to use for any purpose)
- Vacation subsidy available
- Flex Location (possibility to perform our work from different places in the world for a certain period of time)
- Take Time for Charity (additional paid leave of maximum 2 weeks to engage in the charity action of your choice)
- Private healthcare (LuxMed packages)
- Group life insurance (UNUM)
- Multisport
- Stock share purchase additions
- Yearly sales of company laptops and cars