Recommender System Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Recommender System
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Michael S.
Last position:
Data Analyst, Data Scientist at CompuGroup Medical Deutschland AG, docmetric GmbH
Development of AI and traditional models to analyze medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, referral profile analyses, and prescription analyses. Analyses in the field of Real World Evidence.
Healthcare IT, pharmaceutical industry
Tasks, responsibilities:
- Gathering customer requirements
- Planning the subproject
- Designing and defining KPIs
- Designing and developing models and visualizations of results using customer dashboards
- Developing and implementing data warehouse adaptations
- Deriving recommendations for action
Methods, technologies:
- Simulation
- Artificial intelligence
- Python
- R
- SQL
- Microsoft Power BI
- Amazon Web Services
- Elasticsearch
- PostgreSQL
- Databricks
- Multivariate statistics
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Kareem S.
Last position:
Business Analyst & Consulting Manager at S&M Unternehmensberatung PartG
- I support medium-sized clients with their funding needs.
- I evaluate operational business processes and translate complex legal and regulatory requirements into business requirements, target models and actionable roadmap epics.
- I use Generative AI tools and automation in a targeted way to efficiently create requirements documentation, process analyses, evaluations, meeting preparation materials, customer analyses and decision papers.
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Folke V.
Last position:
Nameling – AI-supported product development
- Relaunched a self-developed semantic name recommendation product by combining semantic search, graph-based similarity analysis, LLM-/RAG-supported content, and AI-assisted development processes.
- End-to-end responsibility across the product lifecycle—from use case definition and solution design through prototyping and evaluation to the iterative development of the roadmap.
- Evaluated AI use cases in terms of user value, technical feasibility, data quality, governance, and operating costs to guide MVP scope, roadmap decisions, and continuous product improvement.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Ramazan C.
Last position:
Fullstack-/DevOps Engineer at BKA (Federal Criminal Police Office)
Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.
- Responsible involvement in the design, development, and implementation of new backend and frontend features
- Hands-on development with Java, Spring Boot, Python, and Angular
- Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
- Further development of modern web interfaces with Angular, including connection to backend services
- Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
- Containerization and deployment of applications with Docker, Kubernetes, and Helm
- Support with CI/CD processes and deployment to Kubernetes-based environments
- Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
- Close collaboration with developers, business teams, DevOps, and other technical stakeholders
- Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
- Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation
Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Alyosh A.
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Danny-Michael B.
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Tandi T.
Last position:
Founder, Circular Business Advisor at Circular Business Network
First network for professionals working on circular business models
Founded a global network for professionals working on circular business models to connect, collaborate, innovate, and scale impact.
- Partnered with the City of Munich and Circular Munich on a 3-year strategy to improve data collection and textile reuse in Munich.
- Advised 5 SMEs on how to pilot, scale and optimize circular business models for profitability and sustainability.
- Produced market research on the circular business landscape to highlight opportunities, trends and risks.
Sukumar R.
Last position:
AI Consultant
- Architect end-to-end AI/ML solutions, with a strong focus on advanced LLM applications, autonomous agentic AI workflows using MCP toolset, vLM embedding computations, and robust agentic RAG pipelines to drive real-world business impact for clients across diverse sectors.
- Designed intelligent agentic AI workflows to automate knowledge retrieval and reasoning for B2B clients, accelerating business impact.
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Discover over 15,000 top freelancers
Statistics of experts using Recommender System
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.2 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Retail
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
64%
Doctorate
21%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Germany using Recommender System
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Recommender System experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (89%)
- Professional Services (56%)
- Retail (44%)
- Banking and Finance (41%)
- Media and Entertainment (41%)
- Education (39%)
- Automotive (34%)
- Manufacturing (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
A recommender system predicts which products, articles, videos, jobs or services a person may find relevant. It combines user behaviour, item attributes and context to rank possible choices. Common approaches include collaborative filtering, content-based methods, hybrid models and learning-to-rank.
Where it fits
Companies use recommendation technology in online retail, media, travel, publishing, advertising and marketplaces. It can power related-item carousels, personalised feeds, search re-ranking, next-best-action prompts and tailored email selections. In Germany, teams also apply it to large catalogues and multilingual customer journeys.
- Product and content recommendations
- Personalised homepages and feeds
- Similar-item and complementary-item discovery
- Search ranking and category ordering
Tools and ecosystem
Specialists work across data pipelines, model training, feature stores and serving layers. Typical tools include Python, SQL, Apache Spark, Kafka, Airflow, TensorFlow, PyTorch, scikit-learn and Elasticsearch. They may also use MLflow, Feast, cloud data warehouses and APIs that deliver ranked results to web or mobile products.
When to hire expertise
External expertise helps when recommendation quality is central to a product but internal teams lack the time or specialised knowledge. Companies often need support after collecting behavioural data, during a redesign of search or discovery, or when an initial model performs poorly in production. Germany-based collaboration can suit on-site workshops, while remote delivery works well for clear data and product interfaces.
- Define events, labels and evaluation data
- Select a model and establish a baseline
- Build training and inference pipelines
- Integrate recommendations into product flows
What delivery includes
A project may produce a data model, feature engineering workflow, ranking service, batch or real-time pipeline and monitoring dashboard. Strong professionals also document cold-start handling, feedback loops, privacy boundaries and fallback logic. They connect offline evaluation with controlled product tests rather than treating model scores as the only measure of success.
Signs of strong specialists
Look for professionals who can explain why a recommendation is shown, how bias and popularity effects are controlled and how the system behaves with sparse data. They should understand precision and ranking metrics, but also latency, catalogue changes, experimentation and product constraints. Experience with multilingual data and clear communication in English or German can support distributed teams in Germany.
Frequently asked questions
What clients ask us most about Recommender System — answered in short.
A Recommender System selects and ranks items that may be relevant to a person, such as products, films, articles or job listings. It can support discovery, personalisation, cross-selling, search re-ranking and feed ordering.
A Recommender System predicts relevance from behaviour, item information and context, often without a direct query. Search usually responds to explicit terms, while recommendation can surface useful choices before a person knows what to ask for. Many products combine both approaches.
A strong Recommender System specialist usually understands data modelling, event tracking, SQL, Python and machine learning operations. Useful adjacent skills include Kafka or Spark pipelines, cloud data platforms, Elasticsearch, API design, experimentation and product analytics.
The right level depends on the scope, data maturity and consequences of poor recommendations. A focused proof of concept may need a professional who can establish a baseline and evaluation process, while a production system requires experience with serving, monitoring, feedback loops, cold-start cases and changing catalogues.
Yes. Recommender System work can be delivered remotely when data access, product goals and interfaces are documented clearly. On-site workshops may help with event definitions or stakeholder alignment, while remote collaboration suits modelling, pipeline work and API integration.
A Recommender System based on collaborative filtering learns from interactions between users and items, while content-based methods use item attributes and a person’s history. Collaborative filtering can perform well with rich interaction data; content-based methods can help with new items. Hybrid designs often address the weaknesses of both.
For a Recommender System, review offline ranking metrics alongside coverage, diversity, freshness, latency and behaviour for new users or items. Inspect recommendations manually, test edge cases and define how feedback will be measured after release. A clear fallback is essential when data is missing.
A Recommender System professional should ask which decisions the model influences, what events and item metadata exist, how labels are formed and which constraints apply to privacy, latency and explainability. They should also clarify ownership of data pipelines, deployment, monitoring and product experiments. For teams in Germany, language needs and remote or on-site meeting expectations should be agreed early.
The average hourly rate of freelancers in Germany who have used Recommender System in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Germany who have used Recommender System in their recent projects, 97% hold at least a Bachelor's degree, 64% hold at least a Master's degree, and 21% hold a doctorate.
On average, freelancers in Germany who have used Recommender System in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Germany who have used Recommender System in their recent projects are English (100%), German (98%), and French (25%).
The most common industries among freelancers in Germany who have used Recommender System in their recent projects are Information Technology (89%), Professional Services (56%), and Retail (44%).
The most common business areas among freelancers in Germany who have used Recommender System in their recent projects are Information Technology (97%), Product Development (97%), and Business Intelligence (74%).
Main locations of FRATCH Experts, who have recently used Recommender System
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 
Munich Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
