Feature Engineering Experts in Munich
in minutes from over 15,000 CVs with the power of AIHire experts who turn raw data into reliable features, design feature stores and training pipelines, and reduce leakage in ML workflows. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Feature Engineering
Philipp Grunert
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 Kalinin
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
Raghu Ram Vadali
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 Yapar
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.
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Maziyar Khorrami
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
Caner Karaoğlu
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.
Satish Kore
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.
Azada Henze
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 Uyar
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.
Discover over 15,000 top freelancers
Statistics of experts using Feature Engineering
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 10 years)
Position duration
2.1 years (Germany: 1.8 years)
Positions per freelancer
8 (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
90% (Germany: 82%)
Doctorate
20% (Germany: 14%)
Certifications per freelancer
3
Most common languages
German, English, Turkish
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Feature engineering is the work of turning raw data into inputs that machine learning models can use well. It includes feature creation, feature extraction, encoding, scaling, and time-aware transformations. Strong specialists make data more useful without leaking information or adding noise.
Where it matters
- Fraud detection and risk scoring
- Recommendation and ranking systems
- Forecasting and anomaly detection
- Search, personalization, and segmentation
- Production ML pipelines and feature stores
These tasks show up in product teams, analytics groups, and applied AI projects in Munich, especially where reliable model behavior matters more than quick experiments.
Tooling and stacks
Feature engineering work often sits in Python, SQL, and notebooks, then moves into Spark, dbt, pandas, scikit-learn, and orchestration tools. Many teams also use feature stores, versioned datasets, and validation checks to keep training and serving data aligned.
When to bring in help
Companies usually bring in freelance experts when models underperform, features are duplicated across teams, or training and inference data no longer match. They are also useful when a project needs to move from exploration to a stable pipeline that others can maintain.
What strong specialists do
- Spot leakage and target contamination early
- Design reusable features for multiple models
- Handle missing values, outliers, and skewed data
- Build clear, testable transformation steps
- Keep offline and online features consistent
A strong specialist explains why a feature exists, how it is derived, and how it behaves over time. That discipline is what makes the model easier to trust and easier to run.
How teams work
Feature engineering work can be done remotely, but close collaboration helps when data owners, analysts, and ML specialists need to agree on definitions. In Munich, many engagements combine remote delivery with short local workshops for data review, pipeline design, and handover.
Frequently asked questions
Everything clients usually want to know about Feature Engineering, in one place.
Feature engineering is used to turn raw records into signals a model can learn from. It helps with better accuracy, faster training, and more stable behavior in production. The work often includes encoding, aggregation, scaling, and time-based features.
Feature engineering is broader than basic preprocessing. Preprocessing cleans and prepares data, while feature extraction and feature creation shape it into stronger predictors. In real projects, the lines overlap, so good specialists usually do all three in one workflow.
A company should hire a feature engineering specialist when models are not improving, data definitions are unclear, or teams need a production-ready pipeline. Freelancers are also helpful when a project needs focused work without adding long-term headcount. They can step in for design, implementation, or review.
A strong feature engineering expert usually knows SQL, Python, statistics, and the basics of machine learning. Experience with Spark, dbt, pandas, and feature stores is often useful too. Domain knowledge matters as well, because the best features depend on the business problem.
Feature engineering work often results in transformation logic, feature definitions, reusable pipelines, validation rules, and documentation. In many cases, the specialist also helps define which features belong in training and which can be served online. Clear handover is part of the deliverable.
A feature engineering project can start with one experienced specialist if the data is well understood and the scope is focused. More complex work may need support from data, product, and ML specialists because feature choices affect many downstream systems. The key is practical experience with real data, not just theory.
Yes, feature engineering work is often remote-friendly because much of it happens in code, notebooks, and data reviews. For teams in Munich, on-site time can help when data definitions are messy or when many stakeholders need to align quickly. A mix of remote delivery and local workshops often works well.
A good feature engineering specialist can explain each feature, show how it was tested, and point out where leakage could happen. Look for clean pipelines, reproducible logic, and features that stay consistent between training and production. Strong work is easy to review and hard to break.
The average hourly rate of freelancers in Munich, Germany who have used Feature Engineering in their recent projects is 84 €, which corresponds to a daily rate of about 672 € 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, 90% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Feature Engineering in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.1 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 (30%).
The most common industries among freelancers in Munich, Germany who have used Feature Engineering in their recent projects are Information Technology (70%), Education (60%), and Banking and Finance (50%).
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 (90%), and Research and Development (90%).
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.
Berlin 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
