NumPy Experts in Austria
for reliable scientific computing, matched in minutes with AIHire experts who work with Python arrays, vectorized numerical workflows and scientific data pipelines, with precise matching to vetted, available freelancers for your project.
Meet FRATCH Experts in Austria, who have recently used NumPy
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Alexander L.
Last position:
Guest lecturer in Artificial Intelligence (Master’s Level) at FH des BFI Wien
- Teaching & presenting
- Communication
- Effectively communicate complex technical topics to non-technical audiences through lectures
- Guided non-technical students from zero knowledge to confidently understanding and applying algorithms to achieve business outcomes
Sina D.
Last position:
Computer Vision Specialist at Mission Embedded
- Developing C++ packages for high-frequency video streaming.
- Contributing to the development of assistance systems for industrial applications.
- Designing and implementing sensor fusion solutions.
- Creating simulation models to generate synthetic data for AI training.
- Conducting research on advanced image processing techniques.
Dániel N.
Last position:
Postdoctoral Researcher - Theoretical and Computational Physics at Radboud University
- Built and maintained C and C++ simulation engines with Python analysis for studies of 4D random geometries on shared HPC systems.
- Developed modular Python pipelines with clear interfaces and caching for large datasets to improve analysis throughput and reuse.
- Automated SLURM and PBS batch workflows for submission, monitoring, environment capture, and artifact packaging to ensure reproducibility.
- Refactored utilities into tested, documented packages to lower maintenance effort and support collaboration.
- Supervised BSc students and organized seminars.
- Published several peer-reviewed papers.
Valerio S.
Last position:
Software Engineer at DonTouch SA
Responsible for integrating and maintaining web services for external clients and internal systems, emphasizing automation of ETL pipelines and infrastructure management.
Discover over 15,000 top freelancers
Statistics of experts using NumPy
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
1.8 years
Positions per freelancer
10
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Manufacturing, Education
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
17%
Certifications per freelancer
2
Most common languages
English, German, Persian
Speak two or more languages
100%
Based on our profile pool as of 17 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Austria 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 Austria using NumPy
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 17 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
NumPy 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 (67%)
- Manufacturing (67%)
- Education (50%)
- Banking and Finance (50%)
- Construction (33%)
- Transportation (33%)
- Media and Entertainment (33%)
- Professional Services (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Numerical Foundations
NumPy is the core array and numerical computing library in the Python ecosystem. It provides multidimensional arrays, fast vectorized operations, broadcasting, linear algebra, random sampling and tools for working with structured numerical data. Its ndarray model helps teams process large datasets without writing every operation as a slow Python loop.
Scientific Python Stack
NumPy sits beneath much of the scientific Python stack. Strong specialists connect it with pandas for tabular data, SciPy for advanced algorithms, Matplotlib for visual analysis and scikit-learn for machine learning workflows. They also understand Jupyter, Python packaging, virtual environments and compiled extensions where performance demands it.
Typical Deliverables
- Data transformation and feature preparation pipelines
- Vectorized simulations and numerical models
- Image, signal and sensor data processing
- Array-based components for machine learning systems
- Reusable notebooks, modules and validation tests
NumPy work appears in research software, industrial analytics, finance, energy, manufacturing and life sciences. It can support exploratory notebooks as well as production services that exchange data with databases, APIs, cloud storage or high-performance computing environments.
When Specialists Help
Companies bring in freelance NumPy specialists when a prototype needs to become reliable software, calculations are too slow, or a team needs to replace fragile loops with tested vectorized operations. They can also review memory use, select suitable dtypes, improve interoperability and document numerical assumptions. In Austria, remote collaboration is common, while on-site work can help with laboratory, manufacturing or embedded-data projects.
Quality Signals
- Clear handling of shapes, dtypes, missing values and numerical precision
- Efficient broadcasting and vectorization instead of unnecessary loops
- Tests for edge cases, reproducibility and expected mathematical results
- Sensible profiling before optimizing critical array operations
- Clean interfaces with pandas, SciPy and downstream services
The best professionals explain trade-offs in plain language. They separate exploratory code from maintainable modules, control random state where needed and know when NumPy is enough versus when compiled code, distributed processing or another tool is justified.
Project Collaboration
A NumPy engagement benefits from a clear data contract, representative samples and agreed validation rules. Specialists should be able to work from Austrian or international teams, communicate in the required language and leave behind readable notebooks, tested code and practical documentation. Ask how they investigate numerical discrepancies, profile workloads and preserve results across environments.
Frequently asked questions
Everything clients usually want to know about NumPy, in one place.
NumPy is used for fast array processing, numerical analysis, simulations and scientific data preparation in Python. It is common in machine learning, engineering, finance, image processing and research software.
NumPy focuses on multidimensional numerical arrays and low-level numerical operations, while pandas provides labeled tables and time-series structures. Many projects use both: pandas for data handling and NumPy for efficient calculations underneath.
A strong NumPy specialist often works with Python, pandas, SciPy, scikit-learn, Matplotlib and Jupyter. Depending on the project, SQL, cloud storage, APIs, profiling tools or compiled extensions may also matter.
The required depth depends on the work. Basic transformations may need solid Python and array skills, while numerical models, memory-sensitive pipelines and scientific production systems require stronger knowledge of precision, performance and validation.
Yes, many NumPy projects can be delivered remotely through shared repositories, notebooks, sample datasets and video sessions. On-site collaboration can be useful when the work connects to laboratory equipment, manufacturing systems or restricted data environments.
NumPy is usually preferable when a project performs repeated numerical operations on arrays or matrices. Its vectorized operations, compact data types and established scientific ecosystem can make the implementation clearer and more efficient than nested list processing.
NumPy can support production services when array operations are tested, profiled and integrated behind stable interfaces. A specialist should also assess memory limits, input validation, reproducibility and whether batch, distributed or compiled processing is needed.
Review whether the NumPy code handles shapes, dtypes, edge cases and numerical precision explicitly. Good work includes meaningful tests, profiling evidence, readable structure and documentation that explains both the calculation and its expected results.
The average hourly rate of freelancers in Austria who have used NumPy in their recent projects is 83 €, which corresponds to a daily rate of about 663 € based on an 8-hour working day.
Of the freelancers in Austria who have used NumPy in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Austria who have used NumPy in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Austria who have used NumPy in their recent projects are English (100%), German (83%), and Persian (17%).
The most common industries among freelancers in Austria who have used NumPy in their recent projects are Information Technology (67%), Manufacturing (67%), and Education (50%).
The most common business areas among freelancers in Austria who have used NumPy in their recent projects are Information Technology (100%), Research and Development (100%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used NumPy
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:
- Germany
- Austria
- Switzerland
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