The Senior ML Engineer will spearhead the end-to-end development, deployment, and stewardship of machine-learning solutions that power credit-risk, collections-strategy, conversion-optimisation, and fraud-detection processes in MD Finance. Working hand-in-hand with Risk, Product, Operational and other teams, the role will translate business goals into robust models, ensure their ongoing performance, and identify new AI/ML opportunities that raise the company’s bottom line.

Professional qualifications

7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations.
Demonstrated delivery and productionisation of Probability-of-Default (PD) models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models.
Advanced Python proficiency and solid grasp of modern ML algorithms, feature engineering, and model-evaluation best practices.
Ability to write, structure, and optimise complex SQL queries.
Deep understanding of the credit lifecycle, especially online lending workflows.
Proven skill in sourcing, cleansing, and generating features from data sets.
Comfortable setting up and maintaining modelling environments (local, cloud, or on-prem).
Detail-oriented, accountable, and committed to both team and individual targets.
English: Intermediate (B1) or higher.
But the most important skill for this position is a proficiency with COBOL (Common Business-Oriented Language) programming language!

Preferred / bonus qualifications

Practical experience with LLM solutions:

Using commercial APIs (e.g., OpenAI, Anthropic, etc.).
Self-hosting of open-source models
Fine-tuning of open-source models.
Building voice chatbots.
Building RAG chatbots.

Experience with Computer Vision models for document or image processing.
Building ML pipelines and deploying models to production.
Creating executive dashboards and model reports in Power BI.

Main responsibilities

Design, train, and deploy probability of default models.
Build credit-limit strategies.
Discover and scope AI/ML opportunities that boost efficiency and revenue of thecompany, including collections optimisation, fraud control, conversion lift, etc.
Analyse data sources and engineer features for modelling.
Produce and update internal model documentation.
Implement model monitoring.
Plan and execute A/B tests.
Build Computer Vision pipelines to automate lending workflows.
Develop LLM-based solutions that streamline internal processes or enhancecustomer experience.

Expected results

Implemented probability of default models and credit-limit strategies.
Launched A/B tests for models that potentially can boost the efficiency and/orrevenue of the company.
Thorough, audit-ready documentation for models.

What We Offer

Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter.
Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture.
Partial compensation for medical insurance, sports activities, and foreign language.
Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose.

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