ABOUT US

Pliant is a European fintech specializing in B2B payment solutions. Our modular, API-first platform helps businesses streamline spending, improve cash flow, and integrate payments into their financial workflows. Designed for industries with complex payment needs, such as travel and fleet, Pliant enables greater efficiency, control, and profitability.

We serve two primary customer segments:

• Companies looking to optimize operational processes through intuitive apps and APIs, gaining control, automation, and financial flexibility through extended credit lines.

• Businesses such as financial software platforms, ERP providers, and banks that want to launch or enhance their credit card offerings using Pliant’s embedded finance and white-label solutions.

Founded in 2020 and headquartered in Berlin, Pliant supports over 4,000 businesses and more than 20 partners globally. As a licensed e-money institution (EMI), we issue Visa-powered credit cards in 11 currencies across more than 30 countries, helping companies streamline and simplify payments.

Learn more at www.getpliant.com

ABOUT THE ROLE

As Credit Risk Data Scientist, you will own the design, development, and deployment of data-driven credit models and automated decisioning systems for small and medium sized enterprises. This is a hands-on technical role that sits at the intersection of data science, ML engineering, and credit risk strategy. You will write production ready code, build end-to-end pipelines, and translate model outputs into real credit decisions.

You build things that go live. You own what you deploy. You continuously improve the models, pipelines, and decisioning logic that determine how Pliant extends credit across Europe and the US. You bring both the technical depth to build robust ML infrastructure and the credit intuition to know what good decisioning looks like. If that is you, then join us and work closely with the Head of Risk Strategy and VP of Credit. This position sits directly in the functional domain with exposure to business and full ownership over the outputs you deliver.

This is a hybrid role based in Berlin or London, with potential remote flexibility within the EU/UK depending on team and business requirements.

WHAT YOU'LL DO

• Model development & deployment: Build, validate, and deploy credit risk models owning the full lifecycle from feature engineering and target variable definition through to production deployment, monitoring, and recalibration.

• Data engineering: Design and build end-to-end data pipelines in Python and SQL, integrating internal behavioural data, open banking feeds, bureau data, and third-party sources into scalable, production ready workflows using orchestration tools such as Airflow and dbt.

• Decision engine ownership: Develop, test, and iterate on automated credit decisioning logic translating model outputs into approval, decline, and limit assignment rules within our decision engine, and monitoring their performance post deployment.

• ML infrastructure: Own model deployment, versioning, monitoring, and drift detection. Building the infrastructure that keeps our models performing reliably in production using PSI, Gini, KS, and related diagnostics.

• Portfolio analytics: Analyse portfolio performance, identify risk drivers, and translate empirical findings into actionable credit strategy recommendations.

• Early warning systems: Design and build EWS frameworks that surface deteriorating credit quality early, enabling proactive portfolio management and collections prioritisation.

• Collaboration: Partner with Risk Management, Data, and Engineering teams to build E2E data processes together. Manage cross functional projects and drive delivery.

• Communication: Facilitate smooth and fact based information flow between your colleagues. Support data driven decision making within the credit risk domain. Support the development of a culture of open dialogue, focused on mutual respect and the joint achievement of excellent results.

WHAT YOU'LL BRING

• Degree in a quantitative or engineering discipline or related field.

• 3–5 years of hands on experience in data science, ML engineering, or quantitative credit risk. Production model deployment experience is essential.

• Strong Python capability. You write clean, production ready code. Experience with pipeline orchestration tools such as Airflow or dbt is a strong plus.

• Strong SQL skills for data extraction, feature engineering, and pipeline development.

• Direct experience building and deploying predictive models and monitoring them post-deployment.

• Experience working with APIs, decision engines, and data aggregation and orchestration services is a strong plus.

• Good understanding of credit risk concepts for unsecured SME exposures

• Familiarity with open banking data and transaction level insights is a strong plus.

• Experience with cloud platforms such as GCP, AWS, or Azure and modern data infrastructure tools such as Snowflake or BigQuery.

• Experienced in agile development and the ability to own and drive cross functional projects.

• Determination and desire to work in a team to achieve high quality results for our customers, even under stress.

• Fluent in English; additional European languages are a plus.

WHAT WE OFFER

• The opportunity to work in a growing team with big responsibilities that thrives on a strong exchange of knowledge and excellence

• Attractive remuneration

• Flat hierarchy and transparent communication in a relaxed, professional atmosphere

• Opportunity to develop your talent in a dynamic team with ambitious goals

• Flexibility and possibility to work remotely

• Monthly mobility benefit

• Wellhub Membership

• Pliant Card with monthly credit to explore the product and enjoy food with colleagues

At Pliant, we believe diversity and inclusion are essential to building not only an innovative product but also an exceptional experience for both our customers and our team. This commitment begins with our hiring process—we welcome individuals of all racial and ethnic backgrounds, religions, national origins, gender identities or expressions, sexual orientations, ages, marital statuses, and abilities. If you require accommodations or accessibility support during the interview process, please let us know in your application so we can make sure your experience is seamless.