Founded in 2012 and with HQ in Riga, Eleving Group is a publicly listed international fintech operating across 18 countries on 3 continents.
Our team of 4,600+ people brings together different cultures, perspectives, and experiences. Together we create vehicle, smartphone, and consumer financing solutions for more than 2.2 million registered users worldwide.
At Eleving Group, you’ll think beyond one role, team, or market — working across borders, making an international impact, and seeing your work travel beyond your desk as you grow with the company.
Linards KalvānsHead of Data Science & Engineering Europe
your missionBuild models that move the business across our European operations. Working in a small, agile team, you’ll own your work end to end, partner directly with business, and deliver solutions across credit, fraud, retention, and product innovation - using modern data and AI tools to move fast.
salarystarting from EUR 3,500 gross
teamData science & engineering
locationRiga or Vilnius
your 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
Bonus points for:
- 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’re looking for
- 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
- 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
- Strong sense of ownership — in a small team, nobody else will pick up the loose ends
- Background in fintech, banking, or another regulated data environment is a +
what we offer
- A team that moves fast, uses AI tools daily, and does not wait for perfect conditions to ship improvements
- Health insurance from Day 1
- Performance-based annual 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