Matplotlib Experts in Berlin
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who turn raw data into clear charts, publication-ready figures, and reusable plotting code with Matplotlib, Seaborn, and Python data stacks. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Matplotlib
Wolfram Knan
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
Santina Wey
Last position:
Business Analyst & BI Strategist - Comparison Portal at dataweys (self-employed)
- Assessment of the existing reporting landscape and strategic bundling of needs
- Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
- Building and maintaining data pipelines
Stack: Metabase · ClickHouse · Appsmith · Airflow
Tobias Jaeuthe
Last position:
Design of an AI-Agent-Based ERP System
- Design of an LLM-based agent system to control the ERP software
- Development of agent workflows with LangGraph and PydanticAI
- Planning interfaces between business logic and language models
- Planning agent orchestration
- Prototype development and demonstration
Tools: Python, Pydantic, React, LangChain, LangGraph, Linux
Tushar Rao
Last position:
Research Assistant/Master Thesis at Otto-von-Guericke Universität Magdeburg
- Performed qualitative and quantitative analysis of extracted findings, categorizing themes, evaluating methodologies, and assessing study quality and reliability.
- Produced research reports and evidence summaries communicating key trends, gaps, and opportunities to academic advisors or cross-functional teams.
- Presented findings through well-structured visualizations, tables, and narrative summaries to support decision-making and guide future research directions.
Deependra Pokhrel
Last position:
Data Specialist at Cloud Factory
- As a Data Specialist, I leveraged analytical expertise to transform raw data into actionable insights, driving strategic decision-making and operational improvements. My role encompassed data interpretation, reporting automation, and cross-functional collaboration, utilizing advanced tools such as Microsoft Excel, Power BI, and Python for comprehensive data analysis.
- Implemented Python scripts to validate and reconcile large datasets, reducing manual errors and improving data reliability.
- Utilized Python (Pandas, NumPy, Matplotlib/Seaborn) to automate data cleaning, analysis, and visualization, improving efficiency and accuracy in reporting.
- Developed interactive dashboards in Power BI to present key metrics, trends, and performance indicators, facilitating real-time decision-making.
- Designed and executed automated reports using Excel (Pivot Tables, Power Query, VBA) and Power BI, ensuring data accuracy and consistency across departments.
- Data Analysis: Excel (Advanced Pivot Tables, Power Query), Power BI (DAX, Data Modeling), Python (Pandas, NumPy, Visualization Libraries)
- Automation & Reporting: Power BI Dashboards, Excel Macros (VBA), Python Scripting.
Phil Howson
Last position:
Data Analyst at Applied Analytics Projects
- Designed and implemented end-to-end data workflows (BigQuery + PowerBI), transforming raw datasets into executive dashboards used for KPI monitoring
- Queried and transformed large-scale datasets using Google BigQuery to support analytical use cases and insight generation
Sebastian Papazoglou
Last position:
Postdoctoral Research Associate at Max Planck Institute for Human Development
- Published a peer-reviewed article on comparative analysis of biophysical models in diffusion MRI, impacting ongoing research projects.
- Got SciPy selected for the cover image of the corresponding journal issue.
Philipp Großer
Last position:
Machine Learning Engineer at docmetric GmbH
- Analyzed patient data for various clients
- Developed complex analysis pipelines
- Performed quality assurance on methods
Srikar Kodi
Last position:
Application Developer at Vavili Technologies
- Played a key role in developing templeswiki.com as a Full Stack Developer, building and optimizing multiple pages and microservices to ensure a responsive and user-friendly experience.
- Developed an interactive chatbot integrated with Natural Language Processing (NLP) to enhance user engagement and streamline customer interactions within the application.
- Built a robust ETL pipeline using Python to generate multi-language labels, facilitating seamless content translation across languages.
- Led the QA team by crafting a comprehensive test plan to rigorously test and ensure the application's smooth operation, alongside developing an in-house attendance recording tool to improve organizational efficiency.
Kashaf Khan
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Vignesh Thota
Last position:
Shift Lead at Flink Expansion GmbH
- Analyzed operational data from 400+ daily orders to identify bottlenecks and optimize delivery workflows, achieving 97% on-time delivery rate through data driven process improvements.
- Led cross-functional team of 10+ associates using data insights to improve protocol adherence by 25% and boost productivity by 15% within 3 months.
- Implemented performance tracking dashboards and KPI monitoring systems that improved staff retention by 10% and maintained 100% safety compliance.
- Drove operational excellence through continuous A/B testing of workflow processes and real-time performance analytics.
Ege Paksoy
Last position:
AI Research Collaborator at NPO
- Contributed to the Karakutu project, developing AI-driven tools to analyze news in Turkey.
- Assisted in web scraping, applied NER for entity extraction, and built interactive filtering interfaces (Vue.js, Plotly.js) for entity and location based search.
- Performed sentiment and content-shift analysis to detect editorial influence in modified news articles.
Maurizio Fleischer
Last position:
Python Software Developer at Schönhofer Sales and Engineering GmbH
- Implemented a command line interface (CLI) for integration of REST APIs of various microservices for end users
- Centralized and simplified interaction with services through the CLI
- Implemented a REST microservice for custom data schemas based on an API-first approach
- Developed event-driven control with RabbitMQ to connect to other services
- Deployed services using Docker and Kubernetes and extended the CLI
- Managed complexity and data volume handling through the microservice
Shyam Sundar Rampalli
Last position:
GenAI Engineer at Freelance
- Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
- Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
- Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.
Manasvi Kumar
Last position:
Max-Cut with MQT-Quantum Auto Optimizer (QAOA vs Classical Baseline) at Independent Project
- Formulated Max-Cut as a QUBO and implemented QAOA vs a Simulated Annealing baseline with reproducible parameter sweeps, fixed seeds, and exact checks on small graphs
- Verified both approaches reached the known optimum on all tested instances; deeper QAOA increased runtime with limited gains on these cases
- Tech: Python · mqt.qao · Qiskit · qiskit-algorithms · NetworkX · Matplotlib · NumPy · SymPy
Discover over 15,000 top freelancers
Statistics of experts using Matplotlib
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
1.9 years (Germany: 2 years)
Positions per freelancer
7
Top business areas
Information Technology, Research and Development, Business Intelligence
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
89% (Germany: 85%)
Doctorate
16% (Germany: 19%)
Certifications per freelancer
2
Most common languages
German, English, Hindi
Speak two or more languages
95% (Germany: 98%)
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 Berlin 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 Berlin using Matplotlib
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
What Matplotlib is
Matplotlib is the core Python library for static, animated, and interactive plots. It is used to build line charts, bar charts, scatter plots, heatmaps, and custom figures for reports, notebooks, dashboards, and scientific work. Many teams use it when they need full control over every visual detail.
Where it fits
Matplotlib often sits in Python data stacks next to NumPy, Pandas, and Jupyter.
- Exploratory data analysis in notebooks
- Figures for papers, reports, and slide decks
- Custom visualizations for internal tools
- Visual checks in data science and ML work
When to bring in an expert
Companies call in specialists when charts need to be more than defaults. That includes multi-axis plots, annotation-heavy figures, publication styling, or cleaning up old plotting code that is hard to maintain. Berlin teams often use remote support for data work, while on-site sessions help when plotting is tied to local workshops or stakeholder reviews.
Strong skills around it
A strong Matplotlib professional understands figure layout, axes, legends, color choice, and export formats. They know how to balance readable plots with consistent style across notebooks, scripts, and reporting pipelines. For larger Python projects, they also work comfortably with Pandas indexing, NumPy arrays, and file outputs like SVG, PNG, and PDF.
Signs you need help
- Charts look inconsistent across notebooks or reports
- Labels overlap or legends are hard to read
- You need a house style for many figures
- Old plotting code is slow or brittle
- You need print-ready or presentation-ready output
Typical deliverables
Freelance experts usually deliver plotting modules, reusable helper functions, style templates, and cleaned-up notebooks. They may also rebuild legacy plotting scripts, adapt figures for scientific publishing, or prepare visualization code for a broader Python analytics workflow. The result is code that is easier to reuse and figures that are easier to trust.
Frequently asked questions
What clients ask us most about Matplotlib — answered in short.
Companies hire Matplotlib specialists to turn data into clear charts for analysis, reporting, research, and product work. Common tasks include custom figure layouts, annotation-heavy plots, publication styling, and cleaning up plotting code that has grown messy over time.
Matplotlib is the most flexible choice when teams need full control over every element of a figure. Seaborn is often used for faster statistical defaults on top of Matplotlib, while Plotly is chosen when interactive charts matter more than fine-grained static output. The right freelancer knows when each tool fits the job.
A strong Matplotlib freelancer usually also works well with Pandas, NumPy, and Jupyter. For real projects, they should understand data shaping, figure export, styling, and how plots fit into a broader Python workflow. That mix matters more than just knowing the plotting API.
Simple plotting tasks can be handled quickly, but complex visual standards need a specialist with deeper practice in Matplotlib. If you need multi-panel figures, custom annotations, or consistent styling across many reports, choose someone who has worked on similar outputs before. The harder the visual requirement, the more valuable that experience becomes.
Yes, Matplotlib is widely used for static figures that need to look precise in PDFs, papers, and presentations. It is a strong choice when export quality, typography, and layout control matter more than interactivity. A good specialist will tailor each figure to its final destination.
For most Matplotlib work, remote collaboration is enough because the deliverables are code, figures, and documentation. In Berlin, on-site sessions can still help when teams want direct feedback on chart design, internal standards, or workshops with non-technical stakeholders. The best setup depends on how the visuals are reviewed.
Look for clean code, readable chart design, and figures that stay consistent across use cases. A strong Matplotlib specialist explains layout choices, handles edge cases in the data, and delivers reusable plotting code instead of one-off screenshots. Review how they manage labels, legends, exports, and style reuse.
Matplotlib remains relevant because it gives teams precise control and works well inside the standard Python ecosystem. Even when higher-level tools are used, many projects still rely on Matplotlib for final figures, custom styling, and tricky visual requirements. It is a practical choice when output quality matters.
The average hourly rate of freelancers in Berlin, Germany who have used Matplotlib in their recent projects is 72 €, which corresponds to a daily rate of about 580 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Matplotlib in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 16% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Matplotlib in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used Matplotlib in their recent projects are German (100%), English (95%), and Hindi (20%).
The most common industries among freelancers in Berlin, Germany who have used Matplotlib in their recent projects are Information Technology (80%), Education (55%), and Healthcare (40%).
The most common business areas among freelancers in Berlin, Germany who have used Matplotlib in their recent projects are Information Technology (85%), Research and Development (70%), and Business Intelligence (65%).
Main locations of FRATCH Experts, who have recently used Matplotlib
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 
Frankfurt 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
