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# Senior Data Scientist

- **Company:** Solaris Foundation, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $80,000.0 - $100,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Airflow, Business Analytics Applications, Authentication Protocols, Automation of Tests, Profiling, Continuous Integration, Data Infrastructure, Data Transformation, Fraud Prevention and Detection, Machine Learning, NumPy, Query Optimization, Software Engineering, Solaris (Operating System), Feature Store, Feature Engineering, Sql Optimization, Snowflake, Multi-Agent Systems, Model Validation, Git, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Software Version Control
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pou7io56tj

## About the Role

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 our learning 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.

## Description

* 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.