NumPy Experts in Frankfurt
in minutes from over 15,000 CVs with the power of AI.Hire experts who build reliable array logic, vectorized data workflows, and numerical pipelines with NumPy, pandas, SciPy, and Python notebooks. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, who have recently used NumPy
Polina Schulz
Last position:
Data Migration Lead – Process Automation, Data Engineering & Reporting at Large Public-Sector Bank
Configured and automated data extracts from Oracle databases, achieving 100% data accuracy in a critical migration project, significantly reducing manual errors and accelerating the migration timeline.
Designed and implemented interfaces with Order Management Systems (OMS), enabling seamless and automated data exchange and improving operational efficiency through faster, error-free order processing across business units.
Developed and deployed data extraction workflows to support regulatory compliance and customer reporting, ensuring timely delivery of key reports, reducing manual effort, and increasing customer satisfaction.
Alona Liuzniak
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Olusina Fabunmi
Last position:
Cyber Job Simulation at Deloitte Australia
- Completed a job simulation involving reading web activity logs.
- Supported a client in a cybersecurity breach.
- Answered questions to identify suspicious user activity.
Yevgeniy Österle
Last position:
Tester, Test & Data Analyst at NORD/LB
- Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
- Analyze data in DB tables and views of the DWH using SQL (DB2)
- Independently define, create, and execute test cases in JIRA Xray (SIT)
- Write SQL queries in DB2 to verify data scenarios and mappings
- Create test plans for SAP FSDP and concurrent projects
- Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
- Thoroughly document test results in JIRA Xray
- Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Kevin Müller
Last position:
Freelance Lecturer in Coaching at DSI Education GmbH
- Practice-oriented coaching on core aspects of data science
- Teaching advanced concepts in Python as well as automation (with Make and n8n) and ETL processes with Apache Airflow
- Weekly preparation and delivery of practice-oriented programming courses using real-world examples
- Promoting practical programming skills among participants through interactive exercises and individual support
- Developing didactic materials and adapting content to participants' skill levels
- Close collaboration with the team for continuous improvement of course quality and learning outcomes
Jens Daube
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Rashid Ibragimov
Last position:
Java Developer at IT company
- Data transformations
- IT company with more than 100 employees
- Software production
- Data augmentation and normalization, image transformation, format conversion, merging data from multiple sources
- Toolset: Java, Helm, Kubernetes, Kafka, OpenCV, IntelliJ IDEA, Gradle, Git, Docker, Containers, Scrum
Aparna V Ammanath
Last position:
Data Manager at University of Cologne
- Engineered and automated a data pipeline using GitLab CI/CD for data ingestion, validation, and loading into a central database.
- Developed Python scripts for data validation and transformation, ensuring data quality and compliance with metadata standards.
- Managed the entire data lifecycle from file-based repositories to a structured SQL Server database.
- Worked in an interdisciplinary team to establish a central database for-omics data and ensure reproducibility of computational analyses.
Serge Kruse
Last position:
Controlling Specialist – IRBA / Model Validation at Alte Leipziger Bauspar AG
- Credit risk
- Quantitative methods
- Development and validation of IRBA rating systems
- Analysis and monitoring of the credit portfolio
- Reporting
- Development and validation of models for risk provisioning (PWB)
- Support for internal and external audits
Peka Carmel
Last position:
Data Warehouse Project for a Zoo at Alfatraining
- Created a complete entity-relationship model (ERM) for the future operational database
- Implemented the model using an RDBMS
- Designed and implemented a star schema for inventory management
Discover over 15,000 top freelancers
Statistics of experts using NumPy
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 11 years)
Position duration
1.7 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 8)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Banking and Finance, Information Technology, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
89% (Germany: 81%)
Doctorate
22% (Germany: 17%)
Certifications per freelancer
3 (Germany: 2)
Most common languages
German, English, Russian
Speak two or more languages
100% (Germany: 99%)
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 Frankfurt 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 Frankfurt 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
NumPy work
NumPy is the core numerical library in Python. It gives experts fast n-dimensional arrays, vectorized operations, broadcasting, and linear algebra tools for data-heavy work. Teams use it to turn raw inputs into clean, efficient computations.
Typical use cases
- Data preparation and feature work
- Scientific and engineering calculations
- Simulation, modeling, and optimization
- Matrix-heavy analytics in Python
- Performance refactoring for slow loops
Ecosystem fit
Strong specialists work across NumPy, pandas, SciPy, Matplotlib, Jupyter, and related Python tooling. They know how ndarray shape, dtype, and memory layout affect correctness and speed. They also spot when pure Python should be replaced with vectorized code.
When freelancers help
Companies bring in freelance NumPy experts when data code becomes hard to maintain, slow to run, or inconsistent across teams. That often happens in research projects, analytics backends, finance models, or internal tools that rely on Python calculations.
What strong experts do
A good NumPy professional writes clear array logic, avoids unnecessary copies, and handles edge cases like missing values, broadcasting errors, and axis mistakes. They profile code, simplify transformations, and leave behind work that other Python specialists can extend.
Frankfurt projects
In Frankfurt, NumPy work often sits close to banking, risk, reporting, logistics, and data teams that need dependable Python calculations. Specialists can work on-site for sensitive collaboration or remotely when the scope is well defined and the handoff is clean.
Frequently asked questions
What clients ask us most about NumPy — answered in short.
NumPy is used for numerical work in Python, especially when teams need fast array operations, matrix math, and data transformations. It fits tasks like feature preparation, simulation, scientific analysis, and performance cleanup in code that started as plain Python.
NumPy is the base layer for fast numerical arrays and vectorized operations. pandas adds labeled tabular data handling, while SciPy builds more advanced scientific routines on top. Many projects use all three together, so a strong specialist should know where each one fits.
A strong NumPy specialist usually also knows Python well, especially functions, iteration patterns, and debugging. Useful adjacent skills include pandas, Jupyter, data cleaning, testing, and a basic understanding of linear algebra or statistics when the project depends on it.
A small cleanup task may only need someone who can read existing array code and improve it safely. Larger work needs a specialist who understands vectorization, broadcasting, memory use, and numerical edge cases. The right level depends on whether the code is exploratory, production-facing, or performance-critical.
Bring in NumPy help when Python calculations are getting slow, fragile, or hard for the team to review. It also helps when a project needs a clear numerical foundation before it moves into pandas, SciPy, or a larger analytics stack.
Yes, most NumPy work can be done remotely if the data access, scope, and review process are clear. For Frankfurt teams, on-site time can help when the work touches sensitive datasets, internal stakeholders, or cross-team planning. Many projects use a hybrid setup.
Look for clean array logic, correct shapes and dtypes, and code that avoids unnecessary loops and copies. A strong NumPy professional also explains tradeoffs clearly, writes testable transformations, and shows how the work fits the wider Python stack.
NumPy is often the foundation, but it is rarely the whole stack for a real project. Many teams need pandas for tables, SciPy for advanced routines, and visualization or notebook tools for review. A good specialist knows when to stay in NumPy and when to move up the stack.
The average hourly rate of freelancers in Frankfurt, Germany who have used NumPy in their recent projects is 98 €, which corresponds to a daily rate of about 785 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used NumPy 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 Frankfurt, Germany who have used NumPy in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Frankfurt, Germany who have used NumPy in their recent projects are German (100%), English (90%), and Russian (40%).
The most common industries among freelancers in Frankfurt, Germany who have used NumPy in their recent projects are Banking and Finance (70%), Information Technology (60%), and Education (50%).
The most common business areas among freelancers in Frankfurt, Germany who have used NumPy in their recent projects are Information Technology (100%), Business Intelligence (90%), and Product Development (70%).
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.
Berlin 
Hamburg 
Munich 
Cologne 
Stuttgart 
Dresden 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
