Senior Data Scientist m/w/d

Forto Logistics SE & Co. KG; Berlin; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

Das IT-Systemhaus der Bundesagentur für Arbeit in Berlin sucht einen Senior Data Scientist, der Teil des Data Science-Teams wird und für die Entwicklung von Produktions-ML-Systemen verantwortlich ist. Der Fokus liegt auf der Extraktion und Analyse unstrukturierter Logistikdaten, einschließlich Dokumentenautomatisierung und prädiktiver Analytik.

Der ideale Kandidat verfügt über mehr als 3 Jahre Erfahrung in der Datenwissenschaft sowie umfassende Kenntnisse in Python und LLMs. Der Job beinhaltet die enge Zusammenarbeit mit Produkt- und Ingenieurteams.

Qualifikationen

• Erfahrung in der Entwicklung und Wartung von ML-Systemen in der Produktion.
• Fähigkeit zur schnellen Einarbeitung in neue Tools und Technologien.
• Fundierte Kenntnisse in klassischer Datenwissenschaft und Statistik.

Aufgaben

• Entwerfen und Pflegen von ML-Pipelines für Dokumentenextraktion.
• Verbesserung von LLM-basierten Extraktionssystemen.
• Zusammenarbeit mit Produktmanagern zur Identifizierung von Benutzerbedürfnissen.

Kenntnisse

3+ Jahre Erfahrung in Datenwissenschaft oder Maschinenlernen

Python

LLMs

Datenanalyse

Problemlösungsfähigkeiten

Jobbeschreibung

Senior Data Scientist m/w/d

Arbeitsort: Berlin

Your Role & Mission

As a Data Scientist in the Data Science team at Forto, you will take ownership of production ML systems that extract structured intelligence from unstructured logistics data. You will be a Data Scientist Engineer working closely with the Engineering Manager and Product Manager across three core workstreams: document data extraction (FlashDoc), vocabulary mapping, and rate sheet parsing, while using a combination of LLMs, custom models, and rule-based postprocessing. Your immediate priority is ensuring continuity of existing production systems, but equally important is driving step-change improvements in accuracy through disruptive methods and new technologies when the opportunity arises. Beyond document automation, the team’s roadmap extends into traditional data science territory, demand forecasting, churn prediction, route optimization, and predictive analytics for logistics operations.

What Will You Do

• Design, build, and maintain end-to-end ML pipelines for document extraction, classification, and data enrichment in production.
• Develop and improve LLM-based extraction systems for complex logistics documents (packing lists, booking confirmations, invoices, rate sheets).
• Build prompt evaluation frameworks and feedback-based optimization loops to systematically improve extraction accuracy.
• Train custom in-house models using human-in-the-loop (HITL) data to move from assisted to fully automated extraction.
• Build and maintain semantic similarity models for free-text to standardized TMS vocabulary across ports, terminals, container types, legal entities, and line items.
• Contribute to rate sheet extraction: building carrier-specific parsing logic, postprocessing, and multi-file combination logic.
• Improve pipeline reliability through redesign, testing, monitoring, and alerting for non-deterministic ML systems.
• Evaluate and introduce disruptive approaches (new model architectures, fine-tuning strategies, novel evaluation methods) to achieve step-change accuracy improvements when incremental optimization plateaus.
• Scope and build out the team’s next generation of DS workstreams beyond document automation: demand forecasting, churn prediction, route optimization, and other predictive analytics use cases for Commercial and Logistics teams.
• Partner with Product Managers to identify where DS can solve real user pain points, proactively surface opportunities from the data, and shape product roadmaps with a data-informed perspective.
• Collaborate closely with Engineering teams on integration, infrastructure, and API design to ensure DS outputs are consumed reliably by downstream systems.
• Manage stakeholder expectations: communicate what is feasible given capacity, set realistic timelines, flag risks early, and negotiate prioritization trade-offs across teams

Required Skills and Experience

• 3+ years of professional experience in data science or machine learning engineering;
• Ability to design, deploy, and maintain ML systems in production. Go beyond model development. It includes pipeline architecture, monitoring, reliability, and handling non-deterministic outputs at scale;
• Ability to quickly get onboarded with new tools/ technologies/ problem space;
• Strong use of agentic tools for coding;
• Strong proficiency in Python;
• Hands-on experience with LLMs (prompting, fine-tuning, evaluation) and understanding of their limitations in production environments;
• Strong foundation in classical data science and statistics: regression, classification, time series analysis, data leakage, experimental design, and hypothesis testing;
• Strong analytical and problem-solving skills;

Preferred Skills and Experience

• Experience in logistics, supply chain, or freight forwarding domains;
• Experience working directly with Product Managers and Engineering teams;
• Familiarity with semantic similarity and entity resolution techniques;
• Experience with human-in-the-loop (HITL) workflows and designing feedback loops for model improvement;
• Experience with demand forecasting, time series modeling, or churn prediction in a business context;
• Experience with low volume data setting;
• Experience with route or network optimization (cost, risk, or profitability modeling);