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Senior Data Scientist (f/m/d)

Moss•Berlin
Full-time7-15
👁️ 0 views•📝 0 applications•Posted 10/20/2026•Expires 11/19/2026
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Job Description

At Moss, we give finance professionals the power to automate their day-to-day and make forward-thinking decisions. Our culture is what makes that possible: we play to win, we obsess over quality, and we win as One Moss, and it works.

Moss closed a €35 million Series C in August 2026, crossing a €1 billion valuation, and became one of Europe's fintech unicorns. Join us for what's next. We’re expanding our Data & AI team and looking for a senior/lead contributor who has hands-on LLM app/agent experience and is fluent in classical ML.

You’ll own end-to-end LLM/ML initiatives - from problem framing and modeling to evaluation, deployment, and iteration - directly improving core product workflows (invoice understanding, approval matching, reconciliation, anomaly detection, and user-assist).

Your responsibilities Own LLM/ML pipelines for extraction, classification, anomaly detection, and recommendations. Define evaluation & monitoring: gold sets, automated tests, robustness checks, SLAs, and cost/latency tracking.

Productionize with Platform/Data Eng: bring your models live and iterate/improve until they are adopted by your stakeholders About you 5-10+ years in applied ML with production ownership of shipping models/systems.

Hands-on LLM app/agent experience (tool use/function calling, RAG, structured outputs, guardrails) in production. Strong Python + ML stack (e. g. , scikit-learn, XGBoost/LightGBM, PyTorch/TF) and solid experiment/evaluation skills.

Strategic Problem Solver: You approach ambiguous problems by deeply understanding business context and framing data science solutions that drive meaningful outcomes. You rigorously validate assumptions, identify root causes, and prioritize solving the right problem before building.

End-to-End Executor: You independently own projects from ideation to production, balancing model performance with engineering realities. You ensure solutions are scalable, monitored, and maintained with thoughtful evaluation, deployment, and iterati

Required Skills

LLMPythonscikit-learnXGBoostLightGBMPyTorchTensorFlowanomaly detectionclassificationrecommendationsproduction ownershipevaluationmonitoringcost trackinglatency trackinghands-on LLM app experiencetool usefunction callingRAGstructured outputsguardrailsstrategic problem solvingend-to-end execution

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