Senior Data Scientist

Solaris; Berlin; Vor Ort; EUR 90.000 - 150.000; Vollzeit

Benefits dieser Stelle

Home office budget

Learning budget €1000

Meal allowance

Vacation days 28+

Work abroad up to 12 weeks

Zusammenfassung

Solaris, Europe’s leading embedded finance platform headquartered in Berlin, is seeking a senior data scientist to advance ML models for fraud protection and AML. You will partner with stakeholders to operationalise models, build a central feature store, and scale predictions from batch to online.

The role demands 6+ years in data science within regulated finance, proficiency in Python ML libraries, SQL, and experience with Snowflake, Feast, Tecton, dbt and Airflow.

Qualifikationen

• Degree in CS/Math/Stats/Finance or related.
• 6+ years data science experience in a regulated fintech environment.
• Proficient with pandas, numpy, scikit-learn and XGBoost.
• Advanced SQL skills (window functions, optimization) and Snowpark/Snowflake usage.
• Experience with centralized feature stores (Snowflake Feature Store, Feast, Tecton).
• Knowledge of data transformation tools (dbt, Airflow).
• Software engineering basics: Git, CI/CD, testing.
• Excellent English; German preferred.

Aufgaben

• Develop, operationalise and maintain ML models for fraud protection, AML, and risk.
• Prepare training data with analysts, label data, sample for class balance.
• Build and manage a centralised feature store and feature engineering.
• Experiment, train and evaluate baseline and gradient-boosted models.
• Deploy models and provide online and batch prediction services.
• Set up automated pipelines to monitor drift and re-fit models.
• Share knowledge and mentor teammates.

Kenntnisse

Data science

SQL proficiency

Python libraries

Feature stores

Data orchestration

CI/CD practices

Fraud/Risk domain

English proficiency

German language skills

Ausbildung

Degree in CS/Math/Stats/Finance

Tools

Snowflake

Feast

Tecton

dbt

Airflow

Git

Jobbeschreibung

Solaris is Europe’s leading embedded finance platform, pioneering Banking-as-a-Service to bridge the gap between financial technology and licensed banking. Founded in 2016 and headquartered in Berlin, Solaris enables fintechs, digital ecosystems, and multinationals to embed accounts, cards, lending, and payments directly into their own products, creating seamless financial experiences for their users. As a fully licensed German bank, Solaris combines the highest standards of IT security and regulatory resilience with a modular, highly scalable infrastructure.

Today, Solaris is driving the next evolution of financial services by transitioning into an AI-native banking platform, ensuring European businesses can deploy secure, adaptable financial products at scale.

Why join Solaris?

• We are fundamentally redesigning banking processes around AI orchestration, standardized modular building blocks, and embedded regulatory compliance.
• We combine tech and banking in dedicated hubs - driving the technology infrastructure out of Berlin and banking operations out of Frankfurt.
• Through our internal mobility, growth opportunities, and the Solaris Academy, we offer continuous learning tracks, AI ambassador mentorship, and upskilling to keep your skills ahead of the curve.
• We foster a workplace rooted in integrity, proactive risk management, and equality actively driving initiatives like DEI initiatives.

Your Role

• Development, operationalisation and maintenance of Machine Learning models in close collaboration with the business stakeholders for common risk and financial protection with different latency: Fraud Protection, Compliance & AML
• Training data preparation in close collaboration with the analytics engineers including analysis of vast amounts of transactional logs, data labelling, applying chronological splitting and sampling techniques to handle class imbalances
• Feature engineering operations including common features, cross features, positional features and building a centralised feature store
• Model selection, experimentation and training of baseline and gradient boosted models, evaluating performance and trade offs
• Model deployment and prediction servicing from batch to online in close collaboration with the data infrastructure team
• Continual learning by setting up automated pipelines that monitor population drift and continuously re-fit models on fresh data when performance drops below predefined operational baselines.
• Knowledge sharing and mentoring across the team.

We'd love to see

Depending on your level of experience, your responsibilities and scope of role will range. We don’t care much about fancy titles, but rather about real personal and professional development, as laid out in ourlearning framework. Let’s figure together out how you can contribute to our team.

• Degree in Computer Science, Applied Mathematics, Statistics, Quantitive Finance and targeted Financial Engineering courses.
• Minimum 6 years experience in a role of data scientist in a fast pace environment and regulated industry.
• Proficiency in data science libraries (pandas, polars, numpy, scikit-learn) and gradient boosting frameworks (XGBoost).
• Advanced SQL skills (window functions, query optimisation) and hands on experience in analytical platforms (ideally Snowflake by utilising snowpark).
• Experience working with centralized feature platforms (e.g., Snowflake Feature Store, Feast, Tecton) to prevent train-serve skew.
• Good knowledge of data transformation and data orchestration tools, ideally dbt and airflow.
• Solid understanding of software engineering principles, including version control (Git), CI/CD, and automated testing.
• Payment, Fraud and Risk Domain Expertise:
  • Understand transactions movement, payment payload and authentication protocols.
  • Recognize differences in typologies, spotting anomalies and understanding chargeback and dispute cycles
  • Velocity Features, Device Dynamics and Entity Profiling
  • Financial and Regulatory Guardrails
• Excellent English language skills, and preferable German language skills.
• Very strong communication skills, to both technical and non technical members and ability to explain complex statistical outputs to non technical officers.
• Ability to grasp new business concepts and translate them into technical requirements.
• Crisis communication under pressure in periods of unplanned situations.
• Adaptability and continuous learning.
• Adversarial & Skeptical mindset.
• Ability to mentor and inspire team members.
• Comfortable working with AI tools, thinking critically about AI outputs, and contributing to a culture of responsible AI use. We expect you to demonstrate comfort with AI-assisted workflows and a willingness to continuously develop their AI capabilities as the technology evolves.
• Home office budget.
• Learning & development budget of €1000 per year and a transparent growth framework to support your career goals.
• Competitive salary and a variable remuneration program.
• Monthly meal allowance.
• 28 vacation days, increasing by 2 days after 2 years and 3 days after 3 years with Solaris.
• Opportunity towork abroad for up to 12 weeks per year.

While job ads usually paint an ideal picture of a candidate, studies show that most applicants meet an average of 60% of the criteria. Unfortunately, many promising candidates tend to apply only if they meet all the criteria. So if you think you have what it takes, but don't necessarily meet every single item in the job description, please contact us anyway. We'd love to talk with you and find out if you might be a good fit for us.

At Solaris, we are committed to nurturing an inclusive environment, where all Solarians feel valued, respected and supported. We are dedicated to building a diverse workforce that reflects the diversity of our communities. We are committed to equal employment opportunity regardless of color, ethnicity, religion, sex, origin, disability, marital status, citizenship, or gender identity. We are proud to be an equal opportunity workplace. If you have a disability or special need that requires accommodation, please let us know.

If your application is successful, you will be required to complete pre-employment background checks through our screening provider Zinc. These include, but not limited to identity verification, educational records, references, a criminal record check, etc. The processing is based on Art. 6(1)(c) GDPR. Please see below Privacy Information for Job Applicants for details, including recipients, retention, and your rights.

The annual gross salary range for this position is: