SciPy Experts in Munich
for reliable scientific computing, matched fast with vetted and available freelancersHire experts who solve numerical problems, build scientific data pipelines and optimize Python-based models with SciPy, NumPy and pandas. Get precise, fast matching with vetted, available freelancers for your project.
Meet FRATCH Experts in Munich, who have recently used SciPy
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Felix S.
Last position:
App Developer at XIXUM-Modeler
- Developing a model-based AI where natural language is interpreted as formal relations.
- Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
- Develops all kinds of model solutions.
- Backed by natural language and data annotation.
- Requirements to code and other solutions.
Anton L.
Last position:
Senior Solution Architect
Internal systems with regulatory and security requirements could not be used directly by AI tools for incident triage, troubleshooting, and documentation search. The goal was to securely integrate these systems through MCP while preserving existing acce...
Krithika C.
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 D.
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
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
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).
Stephan S.
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 H.
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 C.
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)
Ali H.
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.
Janusz M.
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))
Discover over 15,000 top freelancers
Statistics of experts using SciPy
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
80%
Doctorate
20%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Munich 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 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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
SciPy 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 (82%)
- Automotive (45%)
- Education (45%)
- Banking and Finance (45%)
- Professional Services (45%)
- Manufacturing (36%)
- Government and Administration (36%)
- Healthcare (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Scientific computing with SciPy
SciPy is an open-source Python library for scientific and technical computing. It extends NumPy with mature tools for optimization, integration, interpolation, signal processing, sparse matrices, statistics and linear algebra. Companies use it to turn mathematical methods into maintainable software and reproducible analysis.
Core capabilities
SciPy covers the numerical building blocks behind many research and production systems:
- Solve optimization, root-finding and curve-fitting problems
- Process signals, images and spatial data
- Work with sparse matrices and scientific graphs
- Perform statistical tests, interpolation and numerical integration
Python ecosystem
Strong SciPy specialists understand how its modules fit into the wider scientific Python ecosystem. They typically combine NumPy arrays with pandas data preparation, Matplotlib visualizations and Jupyter notebooks, then connect models to Python services, testing frameworks and data workflows. Knowledge of scikit-learn can also help when numerical methods support machine learning projects.
Where companies use it
SciPy appears in engineering simulations, laboratory analysis, industrial quality systems, energy modeling, finance, geospatial work and research software. It can support calibration, forecasting, parameter estimation and experiment design. In Munich, specialists may collaborate with local industrial, scientific and technology teams while working remotely or on site.
When to bring in a specialist
Freelance expertise helps when a numerical prototype must become dependable software, when existing calculations are slow or difficult to validate, or when a team needs a clear method for a complex model.
- Review numerical assumptions and algorithms
- Improve performance, stability and memory use
- Turn notebooks into tested, documented modules
- Connect SciPy workflows to production data systems
What strong professionals deliver
The best professionals understand both the mathematics and the software around it. They select suitable algorithms, assess conditioning and error, handle edge cases and explain trade-offs clearly. Look for evidence of reproducible tests, meaningful validation data, readable Python, documented assumptions and results that domain specialists can verify.
Frequently asked questions
The facts hiring teams ask for most often when it comes to SciPy.
SciPy is used for scientific and technical computing in Python. Its modules support optimization, numerical integration, interpolation, signal processing, sparse data, statistics and linear algebra, often alongside NumPy.
SciPy builds on NumPy rather than replacing it. NumPy provides arrays and core numerical operations, while SciPy adds higher-level algorithms for tasks such as optimization, integration, statistics and signal analysis.
A strong SciPy specialist usually works comfortably with NumPy, pandas, Jupyter and testing tools. Depending on the project, experience with Matplotlib, scikit-learn, SQL, cloud services or domain-specific modeling can be equally important.
SciPy work can range from a focused numerical analysis to a production modeling system. The right specialist should show relevant evidence, such as validated algorithms, performance improvements, reproducible notebooks or tested Python packages, rather than relying on generic library familiarity.
SciPy projects are often well suited to remote collaboration because code, notebooks, data specifications and results can be reviewed digitally. On-site work in Munich can add value when specialists need close contact with laboratories, hardware, manufacturing processes or domain teams.
Review how SciPy solutions are validated, documented and tested. A quality specialist explains numerical assumptions, checks error and stability, compares results with known cases and produces code that other Python professionals can maintain.
SciPy is useful for the numerical work around machine learning, including preprocessing, optimization, sparse data and statistical analysis. It does not replace specialized machine learning libraries, so the specialist should know when to use scikit-learn or another tool instead.
A SciPy freelancer should clarify the mathematical objective, input data, accuracy requirements, runtime constraints and expected deliverable. It is also important to understand whether the result belongs in a notebook, a reusable package, a service or an existing scientific workflow.
The average hourly rate of freelancers in Munich, Germany who have used SciPy in their recent projects is 111 €, which corresponds to a daily rate of about 887 € 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, 80% hold at least a Master's degree, and 20% 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 2 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 (45%).
The most common industries among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (82%), Automotive (45%), and Education (45%).
The most common business areas among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (91%), Product Development (82%), and Research and Development (82%).
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
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
