NumPy Experts in Switzerland
for reliable numerical computing, matched in minutes with the power of AIHire experts who build scientific computing workflows, optimize array-based processing and connect Python data pipelines with SciPy, pandas and machine learning tools. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Switzerland, who have recently used NumPy
Mohamad K.
Last position:
Senior Backend Developer at Standing on Giants
- Led architecture and end-to-end engineering delivery for community-driven SaaS platforms serving 2M+ monthly active users.
- Architected and led the migration of a monolithic Python/FastAPI and PostgreSQL database and LangChain with codebase to an event-driven microservices architecture on AWS EKS, sustaining 10x traffic growth from ~150 RPS to 1,500+ RPS with zero re-architecture cycles.
- Defined and enforced engineering standards across services including API contracts, observability baselines, and deployment topology, reducing production incidents by 55% and MTTR from 2 hours to under 25 minutes within 9 months.
- Redesigned the caching and query layer using multi-tier Redis caching and database indexing/partitioning, cutting p95 API latency from 850ms to 180ms (78% reduction) and database CPU load by 45%.
- Built CI/CD platform on GitHub Actions, Terraform, and Kubernetes (EKS) with blue-green and canary rollouts, increasing deployment frequency from ~2/month to 8-12/day and reducing lead time from 10 days to under 6 hours.
- Implemented contract testing, automated load testing, and observability SLOs using Prometheus, Grafana, and OpenTelemetry, raising platform availability from 99.5% to 99.95% (10x reduction in error budget burn).
- Led and grew a cross-functional team of 8 engineers across backend, frontend, and DevOps, scaling headcount from 4 to 8 with 85% retention; owned hiring, onboarding, performance reviews, and career development.
- Partnered with Product, Design, and Client Success leadership as primary technical decision-maker; translated business goals into technical roadmaps and drove build-vs-buy decisions on authentication, search, and AI tooling.
- Introduced AI-assisted development workflows including automated code review and a RAG-based internal knowledge assistant using Graph (GraphRAG, Neo4J), increasing sprint throughput by 30% across two quarters.
- Owned incident command and production support rotation; established runbooks, postmortem culture, and on-call SLOs, reducing weekend paging incidents by 70%.
- Developed and optimized Algorithms using python libraries like Numpy and Pandas.
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Georgios S.
Last position:
Senior Software Engineer at UBS Bank
- Implementations of a code refactoring framework able to refactor thousands of repositories leveraging Generative AI
- Use Python (Django, Flask, FastAPI), Java and Typescript in Azure Cloud (Data Lake, VMs, AI) and GitLab infrastructure
- Mentoring and pair programming
- Obtained Azure AI-900 and AI-102 certifications
Ursula M.
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Alejandro A.
Last position:
AI Researcher & Engineer at Tufa Labs
- Deployed and optimized the inference stack on a multi-node DGX B200 cluster across vLLM and SGLang (serving, throughput and latency tuning).
- Built, with a small team, an internal Python library for LM pretraining covering the full training loop: distributed training with PyTorch FSDP, data pipelines, checkpointing, config and hyperparameter management, and experiment tracking.
- Built and evaluated agent scaffolds on interactive game benchmarks similar to ARC-AGI-3, with metrics for how models plan, explore and adapt across multi-step episodes; classified model errors and fed the findings back into scaffold and evaluation design.
- Researched looped transformer architectures.
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Hrvoje K.
Last position:
Researcher, Investment Research Institute at Pictet Group
- Designed and implemented AI-enhanced macro-factor models improving asset allocation decisions across multi-asset portfolios
- Developed machine learning-based FX trading strategies, integrating macroeconomic and sentiment data to identify profit opportunities
- Models adopted by the investment committee to guide equity and currency exposure
- Collaborated with portfolio managers to translate analytical output into actionable investment insights
Discover over 15,000 top freelancers
Statistics of experts using NumPy
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
2.8 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Education, Banking and Finance, Information Technology
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
86%
Doctorate
43%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Switzerland 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 Switzerland 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 16 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.
- Education (86%)
- Banking and Finance (86%)
- Information Technology (86%)
- Healthcare (43%)
- Manufacturing (43%)
- Automotive (29%)
- Energy (29%)
- Government and Administration (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Foundations
NumPy, short for Numerical Python, is the core array-computing library in the Python ecosystem. It provides multidimensional arrays, vectorized operations, broadcasting, linear algebra and statistical functions. Companies use it to process structured numerical data faster and more consistently than repeated native Python loops.
Typical Workloads
NumPy supports calculations inside research, engineering, finance, energy, logistics and machine learning systems. A specialist may deliver:
- Array transformations for scientific and business data
- Numerical simulations and optimization routines
- Image, signal and sensor data preprocessing
- Reusable analysis components for Python services
Ecosystem
Strong NumPy work often connects with pandas for tabular data, SciPy for advanced scientific routines and Matplotlib for visualization. Professionals may also work with Jupyter, scikit-learn, PyTorch or TensorFlow, depending on the product. Knowledge of Python packaging, testing, profiling and data formats helps turn exploratory calculations into maintainable software.
When to Hire
Companies bring in freelance NumPy expertise when calculations are slow, analytical notebooks must become dependable production components or a team needs support with a numerical pipeline. In Switzerland, collaboration may involve remote delivery across regions or on-site work for research, manufacturing and financial teams. Clear technical English is common, while local language skills can help with close stakeholder collaboration.
Quality Signals
A capable professional understands array shapes, data types, memory layout and broadcasting instead of applying vectorization blindly. They validate numerical accuracy, handle missing or unstable inputs and explain trade-offs between readability and speed. Good specialists also profile real workloads, write focused tests and document assumptions so another team can safely maintain the result.
Project Fit
Look for someone who can connect mathematical requirements with operational constraints. Useful evidence includes clean notebooks, tested Python packages, optimized data transformations and experience integrating NumPy with databases, APIs or scientific formats. Before starting, define expected inputs, output tolerances, performance goals, deployment conditions and ownership of the resulting code.
Frequently asked questions
Not sure where to start with NumPy? These answers cover the essentials.
NumPy is used for fast numerical operations on arrays and matrices. It supports simulations, forecasting, signal processing, image preparation, optimization and the data preparation stages of machine learning workflows.
NumPy provides the fundamental multidimensional array structure and numerical operations. pandas is better suited to labeled, tabular data with columns, indexes and missing-value handling; many Python projects use both libraries together.
NumPy is usually a strong choice when a workload applies the same numerical operation across many values. Array operations can make code clearer and faster, but a specialist should first profile the real workload and confirm that vectorization fits its memory and accuracy requirements.
A strong NumPy professional often also knows Python testing, profiling, packaging and data serialization. SciPy, pandas, Jupyter, visualization libraries and machine learning frameworks are useful additions when the work extends beyond core array calculations.
The right level depends on the risk and scope of the work. A simple transformation may need solid Python and array fundamentals, while scientific simulation or performance-critical production code calls for proven numerical reasoning, testing and profiling practice.
Yes. NumPy projects often work well remotely when requirements, datasets, environments and review practices are documented. On-site collaboration can still be valuable for laboratory, manufacturing or research settings where domain context and secure systems matter.
Ask how the professional handles array shapes, data types, numerical precision, invalid inputs and performance measurement. A strong NumPy specialist can explain design choices, provide focused tests and show how results remain reproducible outside a notebook.
Agree on input formats, expected outputs, tolerances, dataset size, runtime conditions and integration points. For NumPy work, also clarify whether the deliverable is exploratory analysis, a reusable package, a production service component or an optimized processing pipeline.
The average hourly rate of freelancers in Switzerland who have used NumPy in their recent projects is 101 €, which corresponds to a daily rate of about 810 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used NumPy in their recent projects, 100% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 43% hold a doctorate.
On average, freelancers in Switzerland who have used NumPy in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Switzerland who have used NumPy in their recent projects are English (100%), German (86%), and French (43%).
The most common industries among freelancers in Switzerland who have used NumPy in their recent projects are Education (86%), Banking and Finance (86%), and Information Technology (86%).
The most common business areas among freelancers in Switzerland who have used NumPy in their recent projects are Information Technology (86%), Research and Development (86%), and Product Development (71%).
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.
Zurich 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
