Role Overview
We are looking for a hands-on Data Scientist to support our fraud recognition project.
The role owns the data science and machine learning aspects of the solution, from data analysis to model evaluation.
The role is well suited for colleagues with strong analytical, SQL, Python, or software backgrounds who want to grow into a machine‑learning‑focused position.
Your responsibilities:
- Analyze transactional and behavioral data to identify fraud patterns; perform data quality checks and exploratory analysis
- Prepare and document datasets for training, validation, and testing using SQL and Python
- Design fraud-relevant features and translate business logic into measurable signals
- Develop, evaluate, and improve supervised and unsupervised ML models for fraud detection
- Handle imbalanced, noisy real-world data and assess models using fraud-relevant metrics
- Analyze false positive / false negative trade-offs and provide model explainability (e.g. SHAP)
- Collaborate with developers and database teams to integrate ML outputs into the application
- Communicate results clearly to technical and non-technical stakeholders, including the customer
Your Profile:
- Strong Python for data analysis and machine learning (e.g. pandas, scikit-learn)
- Advanced SQL for analytical queries and feature generation
- Strong analytical and structured working style
- Experience with supervised learning as well as unsupervised methods such as clustering, outlier detection, or semi-supervised approaches
- Good understanding of overfitting, regularization, feature or label leakage, and its mitigation
- Hands-on exposure to validation strategies under production conditions
- Experience with cloud-based ML software stack
- Communication skills to understand business needs and generate business value
- Nice to Have
- -Experience with AWS Stack
- -Familiarity with fraud-specific metrics or cost-sensitive modeling
- -Experience with Generative AI (e.g. for experimentation, exploration, or documentation)
- -Basic understanding of software engineering concepts and APIs
How about?
