Data Scientist

Eleving Group is an international fintech company specializing in vehicle and consumer finance, operating a multi-brand portfolio in 17 countries across 3 continents, with a 4300+ strong team. As pioneers in emerging economies, we’re driving change, encouraging financial inclusion and upward social mobility through innovative, easy-to-use financial products.

Strive for a way-way up with the Eleving!

We are looking for a Data Scientist to join our Europe hub. You will join a small, agile, and technically advanced data team — not a large analytics department. That has concrete consequences: you own your models end to end rather than handing them over; decisions get made in days rather than quarterly planning cycles, and there is no layer between you and the business owner who uses your output. We keep the stack modern, and we adopt new tools early, including AI tooling, because a small team only stays effective by being well-equipped.

As a Data Scientist, you will work with various datasets, develop predictive models, and collaborate with cross-functional teams to deliver data-driven insights. You will use machine learning, statistical analysis, and other quantitative techniques to provide business solutions, optimize decision-making, and contribute to product innovation. The core of the role remains classical modelling — credit scorecards, churn and retention models, fraud detection, task prioritization — while AI tools are part of how we build, test, document, and monitor that work.

Key Responsibilities:

• Analyze datasets of structured and unstructured data to derive insights that will drive business strategies
• Build, deploy, and maintain machine learning models to solve business challenges in risk assessment, fraud detection, customer retention, and more
• Collaborate with product managers, risk analysts, and business teams to understand their goals and deliver data-driven solutions
• Take responsibility for the full lifecycle of a predictive model including data selection, model training, evaluation, deployment, monitoring, and retirement, using AI tooling to automate routine parts of that cycle such as monitoring, drift and stability reporting, and documentation
• Perform A/B testing and design experiments to evaluate new features or product offerings
• Utilize various tools and libraries (e.g., Python, R, SQL, etc.) to extract, process, and analyze data
• Use AI coding and analysis assistants extensively across the modelling workflow — exploratory analysis, feature engineering, code review, testing, refactoring, and documentation — while remaining fully accountable for the correctness and reproducibility of what you ship
• Where it is the right tool, apply large language models to specific problems such as document and text extraction, classification, and enrichment of unstructured data feeding into classical models
• Ensure data quality and implement data governance best practices, including responsible and secure use of AI tools with company and customer data
• Present findings and recommendations to stakeholders in a clear and actionable manner
• Stay updated with industry trends, emerging technologies, and data science techniques to continuously improve processes and methodologies

What you will bring:

• Strong Python and SQL, and solid grounding in statistics and classical ML (e.g., Scikit-learn, tree boosting, or similar)
• A track record of building models that reached production and were used to make real decisions — we care about what you have shipped, not the number of years behind it. Background in fintech, banking, or another regulated data environment is a plus
• Experience taking models into production and keeping them healthy there — including validation, stability assessment (e.g., PSI), and monitoring
• Practical, day-to-day fluency with AI coding and analysis assistants (e.g., Claude, Claude Code, GitHub Copilot, Cursor), together with the judgment to review and correct what they produce
• Ability to explain a model and its limitations to a non-technical stakeholder, and the willingness to say when the answer is not yet reliable
• Ownership mindset — in a small team, nobody else will pick up the loose ends

Nice to have:

• Experience with MLOps and orchestration tooling (MLflow, Airflow, dbt, or similar) and with cloud ML services (Azure, AWS, or GCP)
• Containerization (Docker, Kubernetes), event streaming (Kafka, Azure Event Hubs), or API development (FastAPI, Flask)
• Working with large language models via API or in notebooks — prompting, structured output, evaluation
• A degree in a quantitative field, or equivalent demonstrable ability. We do not filter credentials

What we offer:

• A team that moves fast, uses AI tools daily, and does not wait for perfect conditions to ship improvements
• Monthly salary starting from 3500 EUR Gross, based on your experience and qualifications
• Health insurance from Day 1
• Performance-based bonus
• Hybrid work setup with 3 days a week from the office & flexible working hours
• Growth opportunities across regions, teams, and markets
• Support for learning & development
• Active social agenda with team events and company initiatives
• Opportunity to work in a dynamic and innovative environment driving commercial growth