Senior Data Scientist (all genders)

Banxware; Berlin; Hybrid; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading fintech provider in Berlin is seeking a Senior Data Scientist to lead analytics-driven projects across business functions. This role involves deploying AI solutions, collaborating with teams, and influencing data-driven decisions. The ideal candidate should have 5+ years of experience in applied data science, strong business analytics skills, and proven MLOps practices. We offer an attractive salary and a flexible work culture.

Leistungen

Attractive base salary

Participation in Share Options Program

Fresh coffee and snacks

Pet-friendly office

Company events

Qualifikationen

• 5 years experience in applied data science or analytics with end-to-end project ownership.
• Strong experience in business analytics and model deployment.
• Proven track record of deploying machine learning models using MLOps.

Aufgaben

• Lead analytics and modeling projects delivering actionable insights.
• Design, develop, and validate machine learning solutions.
• Support building and productionizing AI systems using MLOps.

Kenntnisse

Applied data science

Business analytics

Machine learning

MLOps practices

SQL skills

Tools

Python

scikit-learn

TensorFlow

PyTorch

Docker

Jobbeschreibung

Your mission

Were hiring a Senior Data Scientist to lead analytics-driven decisioning across core business functions. This role has three primary focuses : business analytics and strategic insight risk and portfolio analytics (last two highly desirable but not necessary) and building production-grade ML / AI solutions including MLOps and LLM-based features. Youll partner closely with our Risk-focused data science team and engineering partners to turn complex data into reliable scalable AI products that drive measurable business outcomes.

What you'll do (key responsibilities)

• Lead end-to-end analytics and modeling projects that deliver actionable business insights for stakeholders (commercial pricing operations credit etc.).
• Design develop validate and deploy advanced statistical models and machine learning solutions for business metrics (propensity churn forecasting CLTV segmentation etc.).
• Support building and productionizing models and AI systems using modern MLOps practices : CI / CD for ML model versioning monitoring automated retraining and scalable serving.
• Architect and implement LLM and other generative AI solutions safely and effectively in production (prompt engineering fine-tuning / adapter strategies retrieval-augmented generation hallucination mitigation guardrails).
• Collaborate with the Risk & Portfolio analytics team to extend models for risk assessment portfolio health stress testing and provisioning. Translate risk requirements into robust model assumptions and validation steps.
• Establish and maintain model governance documentation testing and observability aligned with enterprise standards and regulatory expectations.
• Upskill Banxwares data capabilities including other data scientists and engineers
• Communicate technical findings and trade-offs clearly to senior stakeholders and non-technical partners; recommend measurable business actions.
• Lead pilots and proofs-of-concept; convert successful pilots into production-grade systems in partnership with engineering teams.

Your profile

• 5 years experience in applied data science machine learning or analytics with demonstrable end-to-end project ownership.
• Strong experience in business analytics (churn LTV sales / marketing analytics forecasting experimentation).
• Proven track record deploying machine learning models into production using MLOps practices (CI / CD model serving monitoring).
• Knowledge implementing LLM-based solutions in production (prompting fine-tuning RAG architectures safety / guardrails).
• Hands-on with common ML / AI stack : Python scikit-learn TensorFlow / PyTorch experience with containerization (Docker) and orchestration.
• Solid SQL skills and experience working with production data platforms (cloud data warehouses data lakes).
• Familiarity with model governance testing strategies and performance monitoring.
• Ability to translate analytic results into business recommendations and influence senior stakeholders.

Desirable but not necessary

• Prior experience in Fintech
• Knowledge of regulatory model requirements (model validation explainability fairness documentation) is a plus.
• Experience with feature stores model registries (MLflow Sagemaker) and inference serving platforms.
• Familiarity with cloud platforms (in specific AWS) and data engineering patterns (ETL / ELT streaming).
• Familiarity with experimentation platforms and causal inference methods.

Our offer

• An attractive base salary.
• Participation in our virtual Share Options Program.
• Fresh coffee and snacks in the office.
• Pet-friendly office.
• Company & team events for a strong team feeling.
• Direct collaboration with our C-Level.

You belong with us

• We are currently a team of more than 35 employees from over 10 nations and are big fans of diversity. At Banxware everyone is accepted included and celebrated regardless of sexual orientation cultural background gender religion physical abilities or lack of any of these.
• We actively encourage women to apply : Our co-founder Miriam is a role model in this regard.

Place of work

• This is a Berlin-based position.
• We apply a 2-day-per-week office attendance policy.

About us

Banxware is Europes leading embedded finance provider empowering platforms to offer tailored lending solutions to small and medium-sized businesses (SMEs). We partner with a wide range of platforms where SMEs generate revenue including payment and POS providers eCommerce marketplaces and shop software vendors. Additionally we collaborate with platforms aggregating multiple SMEs such as neo banks logistics providers cash management platforms and more. Our innovative solutions are trusted by top-tier companies like Qonto Deutsche Bank (FYRST) Agicap and JustEat Takeaway (Lieferando). From day one weve operated on an international scale and were proud to embrace a global mindset in everything we do.