Feature Engineering Experts in Munich
matched in minutes from over 15,000 CVsHire experts who turn raw business data into reliable model inputs, design training and serving pipelines, and improve feature quality across production machine learning systems. FRATCH matches you with vetted, available freelancers quickly and precisely.
Meet FRATCH Experts in Munich, who have recently used Feature Engineering
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
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
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Caner K.
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Bengisu Y.
Last position:
Freelance BI, AI & Digital Strategy Consultant at Various Clients
- Delivered AI-driven business and marketing strategies to global clients across various sectors.
- Supported small businesses and entrepreneurs with social media content creation, web design, UX/UI improvements, and digital marketing strategies.
- Automated analytics workflows and developed dashboards to monitor campaign performance and engagement metrics.
- Helped clients enhance their digital presence by combining creative storytelling with measurable insights.
- Advised on AI integration in marketing workflows to boost productivity and creative efficiency.
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Azada H.
Last position:
AI Consultant at Freelance
- Built scalable end-to-end machine learning pipelines for a major telco company, covering feature engineering, model development, deployment, and a Streamlit visualization app.
- Initiated and embedded data science within the Customer Experience team, collaborating daily with stakeholders to deliver end-to-end solutions; under my ongoing support, customer satisfaction score, NPS, remained stable at a record >30pt.
- Advised a client on GenAI tools, AI development strategies, and Responsible AI practices, shaping internal adoption and governance approaches.
Utku U.
Last position:
Combining Neural Fields with Hypernetworks
- Developed a meta-learning approach with a teammate to merge multiple neural fields into a single scene representation using a hypernetwork.
- Implemented and evaluated the method on 2D (MNIST) and 3D (ShapeNet) data, showing faster inference compared to overfitting-based baselines.
Satish K.
Last position:
Sustainability Intern at Forschungszentrum Jülich GmbH
- Developed energy estimation models to estimate electric charging and hydrogen refueling requirements at charging and refueling stations for logistics trucks in Germany.
- Estimated future freight traffic demand for Germany using an in-house transport demand model.
- Designed a network of electric charging and hydrogen refueling stations based on transport model results, supporting data-driven infrastructure planning.
Discover over 15,000 top freelancers
Statistics of experts using Feature Engineering
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 10 years)
Position duration
2 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 6)
Top business areas
Information Technology, Business Intelligence, Research and Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Legal
Bachelor's degree or higher
100%
Master's degree or higher
91% (Germany: 81%)
Doctorate
27% (Germany: 15%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
German, English, Turkish
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich 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 of experts in Munich using Feature Engineering
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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Feature Engineering 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 (73%)
- Education (64%)
- Banking and Finance (55%)
- Professional Services (55%)
- Healthcare (36%)
- Media and Entertainment (36%)
- Aerospace and Defense (27%)
- Automotive (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Turning data into signals
Feature engineering transforms raw data into useful inputs for machine learning models. Specialists select, clean, combine and encode variables so models can identify meaningful patterns. The work may involve customer events, transactions, text, images, sensor readings or operational data, depending on the product and business goal.
Typical deliverables
Feature engineering appears in recommendation systems, fraud detection, demand forecasting, search, churn analysis and risk models. Experts commonly deliver:
- Reusable transformations for structured, text or time-series data
- Feature definitions with clear business meaning and ownership
- Training, validation and serving datasets aligned across environments
- Data quality checks, leakage controls and monitoring rules
Tools and adjacent skills
Strong professionals work across Python data stacks such as pandas, NumPy and scikit-learn, while using SQL and distributed tools such as Spark when data volume or latency requires them. They may also use Feast or another feature store, MLflow for experiment tracking, and cloud data warehouses or orchestration tools. Statistics, data modeling, software testing and machine learning evaluation are closely connected skills.
When companies bring in experts
Companies often need freelance expertise when a promising model performs well in experiments but poorly in production. Other signals include inconsistent definitions between analytics and machine learning, slow dataset creation, unexplained model changes or features that cannot be reproduced. In Munich, specialists may support local product, mobility, manufacturing, finance or life sciences teams remotely or in person, depending on collaboration needs.
Production-ready feature work
Feature engineering must account for data freshness, training-serving skew, missing values, changing categories and future information leaking into historical records. Experienced professionals create versioned pipelines, document assumptions and test transformations before they reach a live model. They connect features to measurable product or operational outcomes rather than optimizing variables in isolation.
Choosing the right specialist
Look for evidence of complete feature workflows, from source data and exploratory analysis through deployment and monitoring. A strong expert explains trade-offs in plain language and can challenge weak labels, biased samples or unsuitable evaluation designs. Ask how they would reproduce a feature set, handle schema changes and prove that an improvement comes from better inputs rather than a flawed experiment.
Frequently asked questions
Everything clients usually want to know about Feature Engineering, in one place.
Feature Engineering prepares raw data for machine learning by creating variables that represent useful patterns. It supports systems such as recommendations, forecasting, fraud detection, classification and ranking, while improving consistency between model training and live predictions.
Feature Engineering goes beyond correcting missing values or invalid records. It converts domain knowledge and historical behavior into model inputs, for example by aggregating events, encoding categories or deriving time-based signals, while data cleaning focuses on making source data accurate and usable.
A strong Feature Engineering specialist usually combines Python, SQL, statistics and machine learning evaluation with data modeling and pipeline design. Experience with pandas, scikit-learn, Spark, cloud warehouses, orchestration and feature stores can be important for production work.
The right level depends on the risk, data complexity and production demands of the project. A focused proof of concept may need strong data preparation and modeling skills, while a live system requires experience with leakage prevention, reproducibility, monitoring and training-serving consistency.
Yes, Feature Engineering is often suitable for remote collaboration because data exploration, pipeline development and documentation can be performed through shared technical environments. On-site sessions in Munich can still help when teams need close work with domain experts, restricted data or existing production systems.
Clarify the prediction goal, available data, latency needs, deployment environment and ownership of the resulting pipelines. For Munich-based teams, it is also useful to agree on remote or on-site collaboration, working language and access procedures before the specialist starts.
Good Feature Engineering is reproducible, documented and tested against leakage, drift and training-serving skew. Review whether the specialist compares meaningful baselines, explains why each feature exists and measures improvements on data that reflects real production conditions.
Yes, Feature Engineering remains important when automated machine learning selects models or transformations. Automated tools can generate candidates, but domain knowledge is needed to define valid signals, prevent future information from entering training data and maintain features reliably after deployment.
The average hourly rate of freelancers in Munich, Germany who have used Feature Engineering in their recent projects is 89 €, which corresponds to a daily rate of about 709 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Feature Engineering in their recent projects, 100% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Munich, Germany who have used Feature Engineering in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Munich, Germany who have used Feature Engineering in their recent projects are German (100%), English (100%), and Turkish (27%).
The most common industries among freelancers in Munich, Germany who have used Feature Engineering in their recent projects are Information Technology (73%), Education (64%), and Banking and Finance (55%).
The most common business areas among freelancers in Munich, Germany who have used Feature Engineering in their recent projects are Information Technology (100%), Business Intelligence (91%), and Research and Development (91%).
Main locations of FRATCH Experts, who have recently used Feature Engineering
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.
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