Recommender System Experts in Germany
matched in minutes from over 15,000 CVs with the power of AIHire experts who design personalized product feeds, ranking models and real-time recommendation APIs across retail, media and digital services. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Germany, who have recently used Recommender System
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
Marc S.
Last position:
Fullstack Developer at PLANT-MY-TREE
PLANT-MY-TREE®-per-order
The application enables Shopify merchants to automatically place tree-planting orders for every incoming order. By integrating ecological contributions directly into the purchase process, the manual effort for tracking and billing reforestation initiatives is eliminated. The system increases transparency for end customers through real-time visualizations of the ecological impact directly in the storefront. The architecture is based on a modular monolith with Spring Boot in the backend and an integrated React app inside the Shopify admin area. The solution uses webhooks to capture order data in an event-driven way and integrates the weclapp ERP system for automated monthly invoicing. An app proxy mechanism provides dynamic statistics such as CO2 compensation and planted trees without any performance loss for the merchant shop.
Tasks:
- Design of the modular software architecture based on Spring Modulith to ensure high maintainability
- Development of the event-driven business logic for evaluating Shopify orders via webhooks
- Implementation of automated invoicing by connecting the weclapp REST API
- Building the frontend using React Router and Shopify App Bridge for native integration
- Design of the database model and implementation of the persistence layer with JPA/Hibernate and Prisma
- Integration of internationalization processes for global use in the frontend and email communication
- Automation of deployment processes using Docker and GitLab CI/CD
Project skills: Java 25, Spring Boot, Spring Security, Spring Modulith, Hibernate, JPA, REST API, PostgreSQL, Maven, Liquibase, React, TypeScript, React Router, Vite, Node.js, Prisma, Zod, Docker, Docker Compose, GitLab CI/CD, Shopify CLI, Shopify App Bridge, Polaris, weclapp, i18next, Lombok, Vitest
Danny-Michael B.
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
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
Serge K.
Last position:
Founder
- Idea, design and initial implementation of a service that aggregates publicly available data to estimate prices for certain types of objects
- Collection of data from different data sources and its normalization. Embedding the data using SentenceTransformer and training gradient boost models
- Implementation of a web service that
- reads arbitrary user text
- extends it if some parts of data are missing
- uses a local LLM model to prepare the data for prediction
- embed the data and call for prediction from the gradient boost model
- uses a LLM model to generate a report for the user request including explanations
- Stack: BigQuery, Vertex AI, Cloud Run (GCP), Python, Terraform, DBT, SentenceTransformer, Qwen
- Minor SEO optimizations
- More to come...
Michael S.
Last position:
Data Scientist at CompuGroup Medical Deutschland AG, docmetric GmbH
Development of AI-based and classical models for analyzing medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, and referral profile analyses. Analyses in the area of Real World Evidence.
- Gathering customer requirements
- Planning the subproject
- Designing and defining KPIs
- Designing and developing models and visualizations of the results using customer dashboards
- Developing and implementing DWH adjustments
- Deriving recommendations for action
Methods, technologies: Simulation, Artificial Intelligence, Python, R, SQL, Microsoft Power BI, Amazon Web Services, Elasticsearch, PostgreSQL, Databricks, Multivariate Statistics
Jochen D.
Last position:
Research Associate at Steinbeis Innovationszentrum Innovation Engineering
Part-time position in a federally funded research project
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.
Mojtaba P.
Last position:
Head of Data Analytics & BI at Urlaubstracker GmbH
- Owned the analytics stack end-to-end across data modeling, cloud setup, access control, cost management, and stakeholder-facing dashboards.
- Built and maintained large-scale data workflows across 20+ APIs and 100M+ rows using GCP, BigQuery, dbt, and Spark.
- Supported product, marketing, finance, and commercial teams with KPI frameworks, reporting layers, and decision support.
- Introduced automation and AI-assisted analytics use cases to improve insight generation and internal workflows.
Discover over 15,000 top freelancers
Statistics of experts using Recommender System
Aggregated from the professional profiles of matched freelancers.
Experience
16 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
96%
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 9 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 9 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 (90%)
- Professional Services (53%)
- Retail (42%)
- Education (41%)
- Media and Entertainment (41%)
- Banking and Finance (39%)
- Automotive (36%)
- Manufacturing (34%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What recommender systems do
A recommender system predicts which products, articles, videos or services are most relevant to each person. It combines user behavior, item attributes and context to rank candidates instead of showing the same catalog to everyone. Companies use it to improve discovery, personalization and customer journeys.
Models and approaches
Experts work with collaborative filtering, content-based methods and hybrid recommenders. They select between batch scoring and real-time ranking, then address cold-start users, sparse interactions, changing preferences and recommendation diversity. Retrieval, ranking and re-ranking are usually designed as separate stages.
Data and tooling
Projects connect event tracking and product data with Python, SQL and machine learning libraries. Common tooling includes TensorFlow, PyTorch, scikit-learn, Spark, feature stores, vector databases and REST or streaming APIs. Strong data contracts, privacy controls and reliable feedback signals matter as much as model choice.
Where companies use them
- Personalized product shelves for commerce and marketplaces
- Content, video, music and news recommendations
- Search ranking, related-item suggestions and next-best actions
- Offers, bundles and cross-sell journeys
- Recommendations embedded in mobile and web applications
In Germany, these systems appear across retail, media, travel, finance and industrial services. Specialists can align remote delivery with local product, data and compliance teams when the work requires close collaboration.
When to bring in a specialist
Freelance expertise helps when a company has interaction data but no reliable ranking pipeline, when a prototype must reach production, or when existing recommendations are repetitive and hard to measure. Professionals can audit event quality, define offline and online evaluation, build serving infrastructure and establish monitoring for drift and bias.
What strong experts deliver
The best professionals connect model quality with business and user outcomes. They explain trade-offs between relevance, novelty, diversity, latency and explainability, and they validate results with meaningful experiments. Look for experience across data preparation, model training, API integration, experimentation and production operations, not only isolated notebooks.
Frequently asked questions
What clients ask us most about Recommender System — answered in short.
A recommender system selects and ranks items that may interest a person, such as products, films, articles or financial services. It supports personalized feeds, related-item modules, search refinement, bundles and next-best-action journeys.
A recommendation engine proactively suggests items based on behavior, context and similarities, while search usually responds to an explicit query. The two systems can share retrieval and ranking components, but their goals, feedback signals and evaluation methods are different.
A strong recommender system specialist should understand data engineering, event tracking, machine learning, experimentation and API delivery. Knowledge of SQL, Python, cloud infrastructure, feature stores and privacy-aware data handling is also valuable.
The right recommender system freelancer depends on the project stage and risk. A prototype may need solid modeling and data skills, while a production service calls for experience with large-scale retrieval, low-latency serving, monitoring and controlled experiments.
Yes, recommender system work is often suitable for remote collaboration because data, code and model services can be shared securely. On-site sessions can still help when specialists must align closely with product, analytics or domain teams in Germany.
A reliable recommender system should be assessed with offline ranking measures and carefully designed online experiments. Review relevance alongside diversity, novelty, coverage, latency, user feedback and business outcomes so that one narrow metric does not hide weak user experiences.
A hybrid recommendation model is useful when behavioral data and item information each provide important signals. Combining collaborative filtering with content features can reduce cold-start problems and improve results when user interactions are sparse or preferences change quickly.
Before building a recommender system, freelancers should clarify the target user action, available events, catalog quality, privacy constraints and serving requirements. They should also agree on a baseline, success criteria, feedback loops and how recommendations will be monitored after launch.
The average hourly rate of freelancers in Germany who have used Recommender System in their recent projects is 95 €, which corresponds to a daily rate of about 756 € based on an 8-hour working day.
Of the freelancers in Germany who have used Recommender System in their recent projects, 96% 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 16 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 (90%), Professional Services (53%), and Retail (42%).
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 (75%).
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
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Philipp Thomaschewski
FRATCH CEO
