Job
- Level
- Senior
- Job Field
- Data
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Berlin
- Working Model
- Onsite
Job Summary
In this role, you will take ownership of ML systems for extracting structured intelligence from unstructured logistics data and develop predictive models for demand forecasting, churn prediction, and route optimization.
Job Technologies
Your role in the team
- 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.
- 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.
- Arbeite eng mit den Engineering-Teams an Integration, Infrastruktur und API-Design zusammen, um sicherzustellen, dass die DS-Ergebnisse zuverlässig von nachgelagerten Systemen genutzt werden.
- Manage stakeholder expectations: communicate what is feasible given capacity, set realistic timelines, flag risks early, and negotiate prioritization trade-offs across teams.
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Our expectations of you
Qualifications
- 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.
- 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.
- Strong stakeholder management skills.
- Vertrautheit mit semantischer Ähnlichkeit und Entity-Resolution-Techniken.
Experience
- 3+ years of professional experience in data science or machine learning engineering.
- Hands-on experience with LLMs (prompting, fine-tuning, evaluation) and understanding of their limitations in production environments.
- Experience in logistics, supply chain, or freight forwarding domains.
- Experience working directly with Product Managers and Engineering teams.
- 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).
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What we offer
- Our team is hard-working, constantly seeking to maximise the impact of their work, but we put our people first, always winning with care.
- We value efficient systems and swift, direct communication.
- We want everyone to have their time to speak, so that we can embrace diverse perspectives to help drive towards solutions always.
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Benefits
Health, Fitness & Fun
Food & Drink
Topics that you deal with on the job
Job Locations
This is your employer
Forto Gmbh
As the world's leading provider of digital logistics technology and services, we are constantly innovating to make your supply chain more efficient and easier to manage. Our easy-to-use and intuitive platform enables our customers to optimize their entire global supply chain, giving them greater control over their operations.
Description
- Founding year
- 2016
- Company Type
- Established Company
- Working Model
- Onsite
- Industry
- Logistics, Transportation
Senior Data Scientist
- Location
- Berlin
- Working Model
- Onsite
- Diversity
- Open for all genders
- English Only
- English only required
