Data Scientist

Role Overview

We are looking for a Data Scientist to build and deploy end-to-end machine learning solutions that power key fintech use cases such as credit risk modeling, customer lifecycle optimization, fraud detection, and collections strategies.

This is a role where your work directly influences key business decisions across risk, growth, and collections. As a Data Scientist, you’ll operate at the intersection of data, product, and engineering—developing scalable, production-level machine learning solutions in a modern Azure ML environment. You will take ownership of the full ML lifecycle, from problem definition to deployment and monitoring, while collaborating closely with stakeholders to turn data into insights that drive real, measurable impact.

Key Responsibilities

Own the full lifecycle of machine learning solutions: problem framing, data exploration, feature engineering, model development, deployment, and monitoring
Develop and productionize models for credit scoring, marketing optimization, collections, and other core business problems
Work with large-scale data to build robust data pipelines and feature datasets
Deploy and manage models using Azure Machine Learning, including experiment tracking, model versioning, and lifecycle management
Collaborate closely with stakeholders (product, risk, marketing, engineering) to identify high-impact opportunities and translate them into data solutions
Design, implement, and analyze A/B tests and experiments, ensuring statistically sound and business-relevant conclusions
Build monitoring frameworks to track model performance, detect data/model drift, and ensure long-term reliability
Ensure models meet regulatory and explainability requirements (e.g., credit decision transparency)
Communicate insights and model behavior clearly to both technical and non-technical stakeholders

Requirements

Degree in Data Science, Statistics, Mathematics, Econometrics, or a related field
Strong programming skills in Python and SQL (R is a plus)
Solid understanding of machine learning techniques and their practical trade-offs
Experience with Azure Machine Learning or similar platforms (AWS SageMaker, GCP Vertex AI)
Experience deploying models into production and maintaining them (monitoring, retraining, versioning)
Strong knowledge of experimentation and statistical methods (A/B testing, hypothesis testing)
Experience with model explainability techniques (e.g., SHAP, LIME), especially in regulated environments
Ability to translate complex analyses into clear business insights
Fluent in English

Nice to Have

Experience in fintech domains such as credit risk, fraud detection, or collections optimization
Experience working with distributed data processing frameworks (e.g., Apache Spark, Databricks)
Familiarity with MLOps practices (CI/CD, model registries, pipeline orchestration)
Experience with feature stores and production data pipelines
Experience working in regulated environments (e.g., GDPR, model validation standards)

We Offer

A Truly Global Workplace – work with professionals from 40+ nationalities, bringing diverse expertise, perspectives, and a collaborative international culture.
Hybrid & Flexible Work – we support work-life balance with remote work options and modern office spaces across Europe.
A Culture of Growth – we invest in your future, offering LinkedIn Learning, mentorship, and professional development programmes, including HiPo and leadership development initiatives to support career advancement.
Financial Growth Opportunities – benefit from our share purchase matching programme, allowing you to invest in your future with matched contributions and long-term financial rewards.
Workation Programme – work remotely from different countries for up to 2 months per year, experiencing new cultures while staying connected and productive.

We may use artificial intelligence (AI) tools to support specific parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses against predefined criteria. These tools assist our recruitment team but do not replace human judgment. All final hiring decisions are made by human recruiters.

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Data Scientist

Key Skills

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