SciPy Experts in Munich
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who build numerical workflows, statistical analysis, and scientific computing pipelines with SciPy, NumPy, and related Python tools. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used SciPy
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Krithika Chand
Last position:
Professional Reorientation at Von Rundstedt
- Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.
Sebastian Dirndorfer
Last position:
Data Scientist at CLADE GmbH
- Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
- Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
- Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution
Stephan Sahm
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Eyasu Habte
Last position:
Data Scientist at Deutsche Bundesbank
- Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
- Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
- Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
- Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.
Daniel Carton
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
Technologies: Kubernetes (K3s, RKE2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3s), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Janusz Mazurek
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Ali Habesoglu
Last position:
Data Annotation Working Student at Gini GmbH
- Participated in various annotation projects for machine learning models, contributing to the development and refinement of AI algorithms.
Discover over 15,000 top freelancers
Statistics of experts using SciPy
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
1.8 years
Positions per freelancer
12
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
89%
Doctorate
22%
Certifications per freelancer
3
Most common languages
German, English, Spanish
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 SciPy
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
Scientific computing
SciPy is a core Python library for scientific computing. It adds tested tools for optimization, integration, signal processing, interpolation, and statistics. Companies use it to turn raw data into models, analyses, and reproducible research code.
What teams build
- Data analysis and simulation scripts
- Optimization routines for engineering and research work
- Signal, image, and time-series processing
- Numerical methods inside Python applications
SciPy is often used with NumPy, pandas, and Matplotlib to move from calculation to reporting.
Ecosystem skills
Strong specialists know the SciPy stack, array handling, vectorization, and numerical stability. They also understand testing, documentation, and how to keep scientific code readable for other experts.
Typical tools around SciPy include Jupyter, NumPy, pandas, Matplotlib, and scikit-learn. In Munich, that mix often fits work in industrial software, mobility, research groups, and analytics teams.
When companies need help
Companies bring in freelance SciPy specialists when a model is unstable, a script is slow, or a research prototype needs to become maintainable code. They also help when internal teams need support for methods selection, refactoring, or validation.
- Fix numerical edge cases
- Speed up array-heavy code
- Review scientific logic and outputs
- Prepare handover documentation
What strong professionals do
A strong SciPy professional does more than call library functions. They check assumptions, choose the right method, and verify results against the problem, not just the code.
They write clear functions, keep dependencies small, and explain limitations early. That matters when the work supports decisions in engineering, biotech, finance, or applied research.
Hiring in Munich
For Munich teams, SciPy expertise is useful in projects that blend Python with local research, industrial analytics, and product development. Many tasks can be handled remotely, but on-site collaboration can help when the work depends on domain review or close stakeholder feedback.
Clear English communication is often enough for international teams. If your work needs regular reviews of formulas, plots, or validation steps, look for specialists who can discuss methods in plain language.
Frequently asked questions
The facts hiring teams ask for most often when it comes to SciPy.
SciPy is used for scientific and numerical work in Python. Teams rely on it for optimization, integration, interpolation, statistics, and signal processing. It is a good fit when the task is more about reliable methods and reproducible results than building a standard business app.
SciPy sits close to NumPy and often uses it for arrays and math foundations. pandas is better for tabular data handling, while scikit-learn focuses on machine learning workflows. A strong specialist knows when SciPy is the right layer and when another library should take over.
A strong SciPy specialist should also know NumPy, Jupyter, plotting tools, and basic statistics. For many projects, Python packaging, testing, and documentation matter just as much. If the work involves research or engineering, domain knowledge is a big plus.
A small cleanup task may need only a specialist who knows the relevant function set and the data shape. Complex work, such as numerical modeling or optimization, needs someone who understands stability, assumptions, and validation. The more business risk the results carry, the more important deep SciPy experience becomes.
Most SciPy work can be done remotely because it lives in Python code, notebooks, and review sessions. On-site time in Munich helps when the project depends on sensitive data, lab context, or fast feedback from domain experts. Many teams use a mixed setup.
Look for clean code, clear explanations, and proof that the specialist checked results against the real problem. Good SciPy work is not just correct output; it is stable, testable, and easy for others to maintain. Ask how they handle edge cases, data quality issues, and validation.
SciPy is common in research, but it is also useful in product teams that need numerical logic inside a Python system. That includes engineering tools, analytics features, forecasting, and simulation-based workflows. The key is to keep the code robust enough for production use.
Ask which parts of the workflow they would review first, how they validate numerical results, and what they do when a method fails on real data. A good SciPy expert should be able to explain trade-offs in plain words. Also ask whether they are comfortable working with your existing Python stack and team process.
The average hourly rate of freelancers in Munich, Germany who have used SciPy in their recent projects is 102 €, which corresponds to a daily rate of about 818 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used SciPy in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Munich, Germany who have used SciPy in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Munich, Germany who have used SciPy in their recent projects are German (100%), English (100%), and Spanish (44%).
The most common industries among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (78%), Education (56%), and Professional Services (56%).
The most common business areas among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (89%), Product Development (78%), and Research and Development (78%).
Main locations of FRATCH Experts, who have recently used SciPy
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.
Countries:
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
