SciPy Experts in Munich
matched in minutes by AIHire experts who develop numerical models, optimize scientific workflows and connect SciPy with NumPy, pandas and scikit-learn. Find vetted, available freelancers whose experience fits your project with fast, precise AI matching.
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.
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)
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))
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.
Discover over 15,000 top freelancers
Statistics of experts using SciPy
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.2 years
Positions per freelancer
12
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 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 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 19 Sep 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 (80%)
- Automotive (50%)
- Education (50%)
- Professional Services (50%)
- Banking and Finance (40%)
- Manufacturing (40%)
- Government and Administration (40%)
- Healthcare (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Scientific computing foundation
SciPy is an open-source Python library for scientific and technical computing. It extends NumPy with tested algorithms for optimization, integration, interpolation, signal and image processing, statistics, sparse matrices and linear algebra. Companies use it to turn mathematical methods into maintainable software.
Models and algorithms
SciPy supports prototypes and production services that analyze measurements, simulate physical systems or solve complex numerical problems. Specialists apply its modules to fit models, calculate integrals, find roots, process signals and work with sparse data. They also assess numerical stability, precision and runtime before a result reaches users or automated systems.
Python ecosystem
SciPy rarely stands alone. Strong specialists combine it with NumPy arrays, pandas data preparation, Matplotlib visualization, Jupyter notebooks and scikit-learn workflows. Depending on the project, they also use SymPy, Numba, Cython, Dask, PyTorch or domain-specific Python packages. Good integration keeps data types, performance and dependencies under control.
Where expertise helps
- Creating numerical prototypes from research formulas or engineering requirements
- Replacing slow Python loops with vectorized or sparse operations
- Building optimization, forecasting, signal-processing or simulation pipelines
- Validating algorithms against reference data and scientific constraints
- Packaging notebooks and experiments into tested production components
In Munich, this expertise can support manufacturing, mobility, medical technology, energy and research-oriented teams. Remote collaboration works well when data, assumptions and acceptance criteria are documented clearly.
When to bring in a specialist
Freelance expertise is useful when an internal team has data but needs a sound numerical method, or when a prototype must become a dependable service. It can also help during a migration from MATLAB, R or custom C and Fortran routines. Clear signs include unexplained model results, unstable optimization, slow array operations or difficulty reproducing notebook findings.
What strong professionals deliver
A strong SciPy professional explains the mathematics behind an implementation and makes its limitations visible. They create focused tests, compare results with known solutions and measure performance on representative data. They understand floating-point behavior, conditioning, sparse representations and reproducible environments. Their deliverables may include clean Python modules, documented notebooks, validation reports, APIs and deployment-ready workflows.
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. It provides algorithms for optimization, integration, interpolation, statistics, signal processing, sparse data and linear algebra, often alongside NumPy.
SciPy builds on NumPy rather than replacing it. NumPy supplies arrays and core numerical operations, while SciPy adds higher-level algorithms for tasks such as optimization, statistics, integration and signal analysis.
A strong SciPy specialist usually works comfortably with NumPy, pandas, Jupyter and Matplotlib. Depending on the assignment, knowledge of scikit-learn, SQL, cloud deployment, testing, Numba or domain-specific mathematics is also valuable.
The right level depends on the numerical risk and the project stage. A focused prototype may need an expert who can select and validate an algorithm, while production work calls for deeper testing, performance analysis, packaging and monitoring around SciPy.
Yes. SciPy work is well suited to remote collaboration when datasets, mathematical assumptions and expected outputs are accessible. Teams in Munich should agree on documentation, repository practices, meeting language and access to any site-specific equipment or data.
SciPy is often a strong choice when numerical methods must live inside a broader Python application or service. MATLAB can be preferable for an established engineering environment, while R may fit statistics-focused analysis; the decision depends on existing tools, deployment needs and team skills.
Ask the specialist to explain algorithm selection, assumptions, error behavior and edge cases. Review tests against known results, reproducibility of the environment, performance on realistic data and the clarity of the SciPy implementation.
A SciPy freelancer may deliver a validated numerical module, a research notebook, an optimization or simulation pipeline, or a production service component. Useful handover material includes tests, dependency definitions, documentation, benchmark methodology and guidance for future maintenance.
The average hourly rate of freelancers in Munich, Germany who have used SciPy in their recent projects is 109 €, which corresponds to a daily rate of about 874 € 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 16 years of professional experience, with a single engagement typically lasting around 2.2 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 (50%).
The most common industries among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (80%), Automotive (50%), and Education (50%).
The most common business areas among freelancers in Munich, Germany who have used SciPy in their recent projects are Information Technology (90%), Product Development (80%), and Research and Development (80%).
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
