NumPy Experts in Zurich
matched in minutes from over 15,000 CVs with the power of AIHire experts who build reliable numerical workflows, optimize array-based computation and connect Python data pipelines with scientific libraries. FRATCH matches you quickly and precisely with vetted, available freelancers for your NumPy project.
Meet FRATCH Experts in Zurich, 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
Discover over 15,000 top freelancers
Statistics of experts using NumPy
Aggregated from the professional profiles of matched freelancers.
Experience
22 years
Position duration
2.8 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
33%
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 Zurich 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 Zurich 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.
- Information Technology (100%)
- Education (83%)
- Banking and Finance (83%)
- Energy (33%)
- Healthcare (33%)
- Manufacturing (33%)
- Government and Administration (33%)
- Retail (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Numerical computing with NumPy
NumPy is the core numerical computing library in the Python ecosystem. Its n-dimensional arrays, vectorized operations and broadcasting rules support scientific analysis, simulations, statistics, optimization and machine learning workflows. It gives teams efficient primitives for working with structured numerical data.
Arrays and performance
Strong NumPy work starts with choosing suitable array shapes, data types and memory layouts. Specialists replace slow Python loops with vectorized expressions, ufuncs and targeted linear algebra operations. They also profile workloads, manage copies and views, and keep numerical results stable as data volumes grow.
Python ecosystem
NumPy connects closely with tools used across data and research teams:
- Integrate pandas for tabular data preparation and analysis
- Use SciPy for optimization, sparse matrices and scientific algorithms
- Feed scikit-learn, PyTorch or TensorFlow workflows
- Combine arrays with Matplotlib, Jupyter and visualization pipelines
- Package reusable numerical utilities for production services
Where companies use it
Companies use NumPy for forecasting, signal processing, image and geospatial analysis, financial models, engineering simulations and experiment pipelines. It often sits beneath analytical products, research tools and Python services rather than appearing as a user-facing feature. In Zurich, specialists may support finance, life sciences, engineering and research teams across remote or on-site delivery models.
When outside expertise helps
Freelance expertise is useful when an existing Python workflow is too slow, difficult to reproduce or hard to move into production.
- Convert nested loops into clear, tested array operations
- Diagnose memory use, broadcasting errors and numerical instability
- Port MATLAB or other matrix-based workflows to Python
- Prepare algorithms for parallel, distributed or hardware-accelerated execution
- Establish tests, documentation and maintainable data interfaces
What strong specialists bring
The best professionals understand both mathematics and software delivery. They explain array semantics clearly, select algorithms rather than applying vectorization blindly, and test edge cases such as empty inputs, missing values, precision limits and unexpected shapes. They also work comfortably with Git, packaging, notebooks, continuous integration and the communication needs of distributed teams.
Frequently asked questions
Key details about NumPy, drawn from the questions we get asked most.
NumPy is used for fast numerical operations on arrays and matrices. Teams apply it to data preparation, simulations, signal processing, statistics, optimization and the computational foundations of machine learning. It is especially useful when many values must be processed with consistent mathematical rules.
NumPy provides compact numerical arrays and low-level operations, while pandas focuses on labeled tabular data with indexes and columns. A project may use pandas for cleaning and joining business data, then rely on NumPy for numerical transformations and array-heavy calculations. The two libraries commonly work together rather than replacing one another.
NumPy specialists often work with Python, pandas, SciPy, scikit-learn and Jupyter. Depending on the project, useful adjacent skills include SQL, data visualization, statistical modeling, C or Cython integration, cloud execution and production testing. Familiarity with domain-specific data is also valuable.
NumPy work can be straightforward for basic transformations, but performance-sensitive or scientific systems require deeper knowledge. Look for a specialist who can reason about shapes, broadcasting, dtypes, memory behavior and numerical precision, then demonstrate those skills on a comparable workload. The required depth depends more on algorithmic risk than on the size of the codebase.
NumPy projects are well suited to remote collaboration when repositories, datasets, environments and acceptance tests are organized. A Zurich team should agree on working hours, documentation standards and communication language before work begins. On-site sessions can still help with domain discovery, access constraints or workshops.
NumPy is usually preferable when data has a regular numerical structure and operations can be applied across whole arrays. It offers clearer mathematical expressions and more efficient computation than repeated list-based loops. Plain lists remain appropriate for heterogeneous records, irregular nested data or simple application logic.
NumPy quality shows in correct results, readable array logic and tests that cover shapes, boundary cases and precision concerns. Ask the specialist to explain memory use, broadcasting choices and performance measurements in plain language. A strong review also checks reproducibility, documentation and how easily the code integrates with the surrounding Python system.
NumPy can support many workflows commonly built in MATLAB, especially matrix operations, data analysis and scientific computation. Migration may also require SciPy, Matplotlib, Jupyter and changes to testing, packaging or deployment practices. The right specialist should assess numerical equivalence, toolbox dependencies and the target users before planning a rewrite.
The average hourly rate of freelancers in Zurich, Switzerland who have used NumPy in their recent projects is 97 €, which corresponds to a daily rate of about 773 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used NumPy in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used NumPy in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Zurich, Switzerland who have used NumPy in their recent projects are English (100%), German (83%), and French (33%).
The most common industries among freelancers in Zurich, Switzerland who have used NumPy in their recent projects are Information Technology (100%), Education (83%), and Banking and Finance (83%).
The most common business areas among freelancers in Zurich, Switzerland who have used NumPy in their recent projects are Information Technology (100%), Product Development (83%), and Research and Development (83%).
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:
Zurich 
Geneva Basel Bern Request a free demo
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