Data Science Consultants in Germany
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Meet FRATCH Data Science Consultants in Germany
Thorsten Matzner
Last position:
Odoo Implementer at N.N.
As project manager for the Odoo implementation at a small company, I was responsible for designing, implementing, and training a fully integrated CRM and accounting system. Through structured requirements analysis, precise data migration, and targeted change management, I was able to complete the rollout in just eight weeks. The project led to a significant reduction in manual tasks, faster business processes, and increased real-time transparency.
Main Responsibilities
Requirements analysis and process mapping Configuration of Odoo modules: CRM, Sales, and Accounting Data migration (Excel/CSV → Odoo) and quality control Creation of workflows, automated email rules, and dashboards Conducting training sessions and providing support after go-live Project coordination (budget, schedule, stakeholder communication)
Key Achievements
Full implementation of the Odoo suite within the set timeframe (8 weeks) 30% reduction in accounting time and 25% acceleration of the lead-to-sale flow 100% customer satisfaction after go-live, based on survey results Successful migration of 100% of existing master data without data loss Establishment of a sustainable support infrastructure (3-month post go-live support)
Impact
Improved decision-making through real-time dashboards and automated reports Increased efficiency and cost savings (≈ €8,000/month) Scalability for future growth (additional modules can be integrated seamlessly) Strengthened sales and finance departments through seamless process integration
This summary highlights how I created concrete and measurable value for the company through structured project work, technical expertise, and targeted training.
Michael Bader
Last position:
Product Analytics Consultant - Trust & Safety at Kleinanzeigen
- Detecting fraud patterns by implementing aggressive anti-fraud rules while maintaining acceptable false positive rates, reducing fraud exposure to users by up to 80%
- Supporting ideation and roll-out of new trust and safety features to block fraudulent activity and increase user awareness for fraud
- Supporting Product, Development and Customer Support with BI reports and further guidance to identify and fight fraud and policy violations
Thore Fahrtmann
Last position:
Data & AI Consultant at ContiTech GmbH
- Consultancy Databricks Lakehouse Platform Architecture
- Migration of existing manufacturing data services to Databricks. Existing services are running on various platforms & tools and are unified on target platform
- Implementation of new Data & AI use cases on Databricks platform (e.g. connecting new systems, building AI Agent prototypes, …)
Tech Stack: Databricks, Azure, PySpark, Python
Enrico Goerlitz
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Felix Klug
Last position:
Senior Consultant Data Science & Engineer at metafinanz Informationssysteme GmbH
- Technical coaching for migration of activities from SAS to the Palantir Foundry platform
- Provided technical consulting and guidance during onboarding, delivered end-to-end knowledge in Palantir Foundry including pipeline usage
- Developed AI-driven tools for analysis of external parameters using machine learning techniques with TensorFlow and PyTorch
- Built an ETL pipeline in Python deployed on AWS and administered a SQL database
- Optimized business processes through process mining with Celonis by building frontend and backend dashboards, delivering data via SAS and SQL, setting up delta loads, and conducting enablement workshops
- Collaborated with sales and recruiting teams to identify new opportunities and assess applicants
- Organized internal and external events to promote teamwork and strengthen company presence
- Deepened technical skills in AI/ML, cloud-based solutions, and data engineering within the finance and reinsurance industry
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Kai Sieveke
Last position:
Demand Manager, Analyst, Process Consultant
- Integrating system architecture, business analysis, requirements engineering, and process consulting
- Managing business unit needs toward IT and implementation
- Capturing requirements in JIRA and breaking them down into epics
- Overseeing internal projects and programs, including stakeholder management and reporting
- Handling requirements from traditional IT developments to IoT integrations and SAP subsystem replacements
- Implementing current legal regulations (MAKO, EnWG, EEG, GWG, StromGVV, GasGVV, StromNEV, GasNEV)
- Applying agile methods (Agile, SAFe, ITIL, Scrum, Kanban, DDD, IaC, CI/CD, DevOps, automation, ETL, OOA, OOD, MDA, BPMN, BPM, UML, marketing automation, data science, ML, AI, GenAI, LLMs)
- Using tools like JIRA, SharePoint, MS Office, MS Project, MS Dyn CRM, VMware ESX/ESXi, BSI IT-Grundschutz, BSI C5, NIST, MS Azure, Typo3, mail automation, Docker, Kubernetes, OpenStack, OpenShift, Terraform, Ansible, SQL, REST, SOAP, Git, GitLab, LoRaWAN, SAP IS-U, S/4HANA, USU, KUGU, AbSys, sensors, MQTT
Mathew Divine
Last position:
Data Science Expert and AI Strategist at Freelancer
- Built an API to ingest, clean, translate, and index EU tenders documents in Neo4j, enabling hybrid search with RAG and Cypher queries via a Streamlit dashboard
- Deployed the API on AWS Lightsail container services with CI/CD automation via GitHub Actions, ensuring stability through pytest unit and integration tests
- Designed and developed a comprehensive online course on data analysis using ChatGPT for professionals and learners, creating instructional videos and interactive Jupyter notebooks
- Utilized OBS and professional audio equipment to ensure high-quality video and audio content
- Led a CRM data normalization and cleaning project visualized via a Sankey diagram to aid customer understanding and pipeline development
- Implemented and validated a genAI-driven web crawling strategy on AWS, ensuring data quality, scalability, and CRM data augmentation
Robin Steinkühler
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65× faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Marcel Meyer
Last position:
Cloud-Architect, Senior Solution Architect, Senior Software-Engineer at Assignment of KPIs for the service landscape to record and analyse costs per user
- Technologies: GoLang, JavaScript, TypeScript, AWS, Terraform, Git
- Conception of AWS infrastructure and existing services
- Analysis of IAM accounts and roles
- Setup of Cost Explorer and CloudWatch monitoring
- Setup of DynamoDB and S3 persistence of collected information
- Reporting and cost calculation
- Conception of Terraform deployment
Stefan Seidel
Last position:
Consultant IT Application Development & Data Science at Eurofins Finance Transactions Germany GmbH
- Consultant for IT application development and data science
Karsten Courtin
Last position:
Freelance Adobe Analytics Expert at David & Martin
Chaitanya Kumar Dondapati
Last position:
Data Science Consultant at Volkswagen AG
- Designed and deployed GDPR-compliant data pipelines.
- Developed machine learning algorithms for after-sales analysis, improving repair detection.
- Built cloud-based data lake architecture, enabling cross-functional digital transformation.
Alison Vanzetta
Last position:
Data & AI Project Manager at PINKTUM
- Leading an internal technical team in developing an AI-based e-learning solution
- Building business cases, defining requirements, and creating wireframes for new feature enhancements
- Capturing and aligning project goals with internal and external stakeholders
Rudy Pastel
Last position:
Data Science Consultant at Rudy Pastel Consulting
- 08/2021–now: Development of R packages and R-Shiny dashboards, maintenance and enhancement of the ETL software I developed
- 06/2023–12/2023: Migration of the codebase from R 3.6.3 to R 4.3.1
- 03/2019–07/2021: Development of an ETL software using R
- 02/2019–09/2019: Launch of a new product using R-Shiny
- 05/2018–09/2018 (Lowell Financial Services GmbH): Accountants now use the debt collection predictor I built through the GUI I developed
- 07/2018–08/2018 (Symrise AG): Redesign of modeling scripts into R packages
- 12/2019–01/2020 (Symrise AG): Development of a data-driven predevelopment tool with a GUI for flavor developers
- 11/2022–12/2022 (BDO AG): Review of a startup's R codebase as part of the technical due diligence team for a pharma giant
- 07/2023–09/2023 (BMW AG): Scientific and technology scouting for automated test case generation to validate cyber-physical systems
Technologies: R, devtools, testthat, roxygen2, httr, RCurl, Rmarkdown, R-Shiny, SQL, Git
Discover over 15,000 top freelancers
Data Science Consultants statistics
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
1.8 years
Positions per freelancer
13
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
64%
Doctorate
14%
Certifications per freelancer
6
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 27 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this role in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates for Data Science Consultants in Germany
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 27 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the role
What they do
A data science consultant turns business questions into analytical work that can be used in production or in decision making. They define the problem, inspect the data, build models, test assumptions, and explain what the results mean for the business.
Typical deliverables include:
- Problem framing and analytics roadmap
- Data exploration and feature design
- Predictive models and forecasting logic
- Experiment design and evaluation
- Clear recommendations for business and product teams
Core skills
Strong data science consultants combine statistics, machine learning, and practical business thinking. They know when a simple model is better than a complex one, and they can defend their approach in plain language.
- Python, SQL, and common data science libraries
- Model selection, validation, and error analysis
- Data cleaning, transformation, and feature engineering
- Storytelling with data for non-technical stakeholders
- Understanding of business metrics, not just technical scores
Tools and focus areas
The right consultant is usually comfortable across notebooks, data warehouses, and cloud environments. Common work includes customer analytics, demand forecasting, churn analysis, recommendation systems, anomaly detection, and natural language processing.
In Germany, companies often look for freelancers who can work with mixed teams across product, engineering, and management. That matters in manufacturing, retail, finance, logistics, and B2B software, where data science must fit real operational constraints.
When to bring one in
Companies hire a freelance data science consultant when the work is specialized, urgent, or tied to a specific initiative. It is a strong fit when an internal team needs extra expertise, a second opinion, or help moving from analysis to implementation.
- You need support for a short, clearly defined project
- The team lacks deep modeling or experimentation skills
- Data is available, but insight is not yet turning into action
- You need help choosing the right method, not just running one
- The work must connect technical results to business impact
What strong consultants look like
A strong data science consultant asks sharp questions early. They challenge weak assumptions, spot data quality issues quickly, and keep the scope tied to the decision the client needs to make.
They also know the difference between a model that looks good in a notebook and one that holds up in real use. Good consultants document their work, explain trade-offs, and make handover easy for internal teams or developers.
Working style
Many data science projects can be done remotely, especially when access to data and stakeholders is well organized. On-site time can help when the work depends on sensitive data, workshop sessions, or close collaboration with business leads and engineers.
For companies in Germany, language needs vary by team. Some projects run fully in English, while others require a consultant who can discuss findings clearly in German with operations, management, or local stakeholders.
Frequently asked questions
Quick answers to the questions that come up most around Data Science Consultants.
A Data Science Consultant helps a company turn data into decisions, models, and measurable business action. That can include defining the problem, cleaning and analyzing data, building predictive models, and explaining the results to non-technical teams. In many projects, the consultant also helps choose the right method before any modeling starts.
Look for strong Python and SQL skills, solid statistics, and a clear understanding of model validation. A good consultant should also be able to work with feature engineering, experimentation, and data quality issues. Just as important, they should explain trade-offs in simple business language.
A data science consultant focuses on analysis, modeling, and decision support, while a data analyst usually spends more time on reporting and dashboards. A data engineer builds and maintains pipelines, storage, and data infrastructure. In practice, these roles overlap, but the consultant is usually the one who frames the problem and turns data into a method or model.
A freelancer makes sense when the need is project-based, urgent, or very specific, such as forecasting, churn modeling, or experimentation setup. It is also useful when you need senior guidance but do not want to add a full-time headcount. Many companies hire a DS consultant to move faster or to complement an internal team.
Most work can be done remotely if the data access, security setup, and communication are clear. On-site collaboration helps when the project needs workshops, access to sensitive systems, or close work with business teams. In Germany, many clients prefer a mix of both depending on the stakeholder group and the subject matter.
Forecasting, churn prediction, customer segmentation, anomaly detection, recommendation logic, and NLP projects are common fits for a Data Science Consultant. The role is also useful for experimentation design, model review, and analytics strategy. Any project that needs structured thinking around data and business value is a good candidate.
A strong consultant asks precise questions, spots weak assumptions, and explains why a method fits the problem. They should talk clearly about data quality, validation, and the limits of the model, not just the model itself. Good signs are practical recommendations, clean handover, and examples of work that led to real decisions.
It depends on the project, but the best data science consultant usually combines all three to some degree. For model-heavy work, deep machine learning skills matter; for business analysis and decision support, statistics and problem framing are often more important. In many cases, a versatile consultant is better than someone who only knows one narrow toolset.
The average hourly rate for Data Science Consultants in Germany is 116 €, which corresponds to a daily rate of about 925 € based on an 8-hour working day.
Of the freelancers working as Data Science Consultants in Germany, 100% hold at least a Bachelor's degree, 64% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers working as Data Science Consultants in Germany have 16 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers working as Data Science Consultants in Germany are German (100%), English (100%), and Spanish (27%).
The most common industries among freelancers working as Data Science Consultants in Germany are Information Technology (93%), Banking and Finance (67%), and Education (60%).
The most common business areas among freelancers working as Data Science Consultants in Germany are Information Technology (93%), Business Intelligence (87%), and Product Development (67%).
FRATCH Data Science Consultants main locations
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
