NumPy Experts in Berlin
matched in minutes by AIHire experts who turn NumPy into dependable numerical workflows, scientific computing solutions and machine learning data preparation. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Berlin, who have recently used NumPy
Michael B.
Last position:
Product Analytics Consultant - Trust & Safety at Kleinanzeigen
- Detecting fraud patterns and implementing effective anti-fraud rules while maintaining acceptable false positive rates to ensure a balance between safety and a frictionless user experience
- Supporting ideation and roll-out of new trust and safety features to the platform to block fraudulent activity and increase user awareness for fraud
- Supporting Product, Development and Customer Support with BI reports and further guidance to identify and fight fraud and policy violations
Abed D.
Last position:
Co-Founder, Product Manager at HODL It!
- Cut first-30-day post-subscription churn 45% to 20% by revamping onboarding and optimizing time-to-value.
- Drove 3x LTV in 6 months through retention and monetization experiments across the customer lifecycle.
- Owned app redesign and feature delivery leading to lifting active-user NPS from 6.3 to 8.5.
Mukund B.
Last position:
Voice AI Chatbot - Real-Time Audio Assistant
- ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.
Lasya M.
Last position:
Data Engineer at Carelon Global Solutions (Elevance Health)
- Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
- Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
- Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
- Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
- Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
- Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
- Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
Uditha W.
Last position:
Data Science Tutor at University of Europe for Applied Sciences
- Taught Python, Pandas, data analysis, visualization and Power BI to 250+ students.
- Guided practical projects from data preparation and exploratory analysis through visualization and dashboard creation.
- Explained complex analytical concepts clearly to audiences with different levels of technical experience.
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Dilip G.
Last position:
Freelance Computer Vision Consultant at Spiral Physical Therapy Inc.
- Developing methods for monocular 3D facial reconstruction and personalized geometric modelling from mobile imagery
- Building learning-based approaches for facial shape estimation, video-based facial analysis, and privacy-preserving visual learning
Kornél L.
Last position:
Computer Vision Algorithm Engineer (contract) at Sony R&D Center, Stuttgart Laboratory 1
- Conducted research and development in the field of large-scale 3D reconstruction
Mark W.
Last position:
Independent IT/AI Consultant at Freelance
- IT consulting, coaching, and implementation with a focus on AI
Robert F.
Last position:
Interim Director of Accounting at Foodspring
- Leading accounting team of 6 FTEs
- Training and development of employees
- Automation of accounting processes with SQL, Python and PowerQuery
- Owning year and month end close
- Taxation topics
- Audits
- Netsuite Admin and further development
Mathias W.
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Ibrahim H.
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Nino S.
Last position:
Freelancer in Data Science at International Companies
Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients
Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems
Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins
Discover over 15,000 top freelancers
Statistics of experts using NumPy
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
1.8 years
Positions per freelancer
7 (Germany: 8)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98% (Germany: 99%)
Master's degree or higher
84% (Germany: 83%)
Doctorate
14% (Germany: 17%)
Certifications per freelancer
2
Most common languages
German, English, Arabic
Speak two or more languages
96% (Germany: 99%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin 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 Berlin 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 29 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 (88%)
- Education (44%)
- Healthcare (38%)
- Professional Services (34%)
- Retail (24%)
- Automotive (20%)
- Banking and Finance (20%)
- Energy (16%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Numerical foundations
NumPy is the core numerical computing library in the Python data ecosystem. It provides multidimensional arrays, vectorized operations, broadcasting, linear algebra, random sampling and fast routines for working with structured data. Companies use it to build analytical workflows, simulation models, scientific applications and the numerical foundations of machine learning systems.
Ecosystem and tooling
NumPy specialists work across the wider Python stack. Their toolkit may include pandas for tabular data, SciPy for scientific algorithms, Matplotlib or Plotly for visualization, and scikit-learn for classical machine learning. Strong knowledge of Jupyter, Python packaging, testing, profiling and reproducible environments helps turn exploratory notebooks into maintainable software.
Typical deliverables
- Array-based data transformations and feature preparation
- Numerical models, simulations and optimization routines
- Scientific analysis notebooks converted into production workflows
- Performance improvements through vectorization and memory-aware design
- Interfaces between NumPy data and databases, APIs or machine learning pipelines
When expertise matters
Companies bring in freelance NumPy experts when calculations are slow, data workflows are difficult to maintain or research code must become a reliable product. They can help validate numerical assumptions, redesign inefficient loops, structure reusable modules and connect analysis work with existing Python services. In Berlin, remote collaboration is common, while on-site work can support close cooperation with research, manufacturing, mobility or finance teams.
Quality signals
A strong professional understands both the mathematics and the software around an array operation. They choose suitable data types and shapes, handle missing or invalid values deliberately, and measure performance instead of optimizing by guesswork. Look for clear tests, reproducible examples, readable documentation and an ability to explain numerical trade-offs to technical and non-technical stakeholders.
Project collaboration
NumPy work often sits between research, data engineering and application development. Effective specialists clarify expected inputs, output precision, data volume, runtime constraints and deployment conditions before writing the core logic. They can work remotely with distributed teams and communicate in English; German may help when collaborating closely with local stakeholders in Berlin. The best engagement leaves behind tested code, documented decisions and a workflow others can extend.
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 in Python. Companies rely on it for data preparation, simulations, statistics, signal processing, scientific analysis and the numerical steps inside machine learning workflows.
NumPy focuses on homogeneous multidimensional arrays and numerical operations, while pandas provides labeled tables and data-frame features. They are often used together: pandas handles business-oriented tabular data, and NumPy supports the underlying calculations and array transformations.
A strong NumPy freelancer often also knows Python testing, profiling, packaging and data structures. Depending on the project, useful adjacent skills include pandas, SciPy, scikit-learn, visualization libraries, SQL, cloud environments and scientific or domain-specific mathematics.
The right level depends on the work. Basic transformations may need solid Python and array knowledge, while simulations, numerical optimization or performance-sensitive systems call for deeper mathematical understanding, testing discipline and experience diagnosing memory or precision problems.
NumPy projects are usually well suited to remote collaboration because code, notebooks, tests and data contracts can be reviewed digitally. On-site sessions in Berlin can still help when the work involves research teams, sensitive data, hardware integration or frequent workshops with local stakeholders.
Ask for clear array shapes, explicit data types, meaningful tests and evidence that performance was measured with representative data. A capable NumPy professional should also explain numerical accuracy, edge cases, memory use and why vectorized operations are appropriate for the workload.
NumPy can support production systems when its inputs, outputs and numerical behavior are controlled and tested. It commonly forms part of a larger service or pipeline, but teams should also consider deployment, dependency management, monitoring, data validation and whether specialized tools are better for distributed or accelerated workloads.
A NumPy specialist should clarify the data shape, expected precision, performance target, invalid-value policy and integration points. They should also ask how the result will be tested, deployed and maintained, especially when moving from an exploratory notebook into a shared codebase.
The average hourly rate of freelancers in Berlin, Germany who have used NumPy in their recent projects is 83 €, which corresponds to a daily rate of about 665 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used NumPy in their recent projects, 98% hold at least a Bachelor's degree, 84% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Berlin, Germany who have used NumPy in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Berlin, Germany who have used NumPy in their recent projects are German (98%), English (98%), and Arabic (12%).
The most common industries among freelancers in Berlin, Germany who have used NumPy in their recent projects are Information Technology (88%), Education (44%), and Healthcare (38%).
The most common business areas among freelancers in Berlin, Germany who have used NumPy in their recent projects are Information Technology (90%), Product Development (72%), and Business Intelligence (64%).
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.
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Nuremberg Request a free demo
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