Plotly Expert in Germany
for clear, interactive data products, matched with vetted freelancers in minutesHire experts who create interactive charts, analytical dashboards and Python-based data applications with Plotly, Dash and connected data tools. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Germany, who have recently used Plotly
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Manoj K.
Last position:
Data Analyst Work Student at Biebelhausener Mühle seit 1647 GmbH
- Managed and maintained daily sales and transaction data, ensuring data accuracy and integrity for operational reporting and analysis.
- Analyzed customer purchasing patterns to support inventory planning and improve product availability.
Julian H.
Last position:
Working Student / Master's Thesis Student in Software Development at Scale GmbH
- Tech stack: Python, Qt, tensorflow.js, plotly, Angular, TypeScript, HTML, CSS, Git
Nomaira F.
Last position:
Freelance Product Designer / Design Engineer at Independent
- Design and build client web apps and self-directed products end-to-end, covering product design, design systems, and full-stack front-end on React, TypeScript, Tailwind, and Supabase.
- Designed and shipped a compliance and staff-records web application for a UK transport operator, covering data modelling, UX, and front-end build on Supabase with row-level security and Edge Functions.
- Built Merita, a private-by-default achievement-tracking and performance-review app, in a 48-hour hackathon (SheBuilds). It covers win logging linked to goals and competency frameworks, AI-assembled review packets with token-gated sharing, and a Promotion Readiness view, and is built on React, TypeScript, Tailwind, shadcn/ui and Supabase with row-level security and Edge Functions.
- Designed and built Pantressa, a grocery-tracking, meal-planning and budgeting app, owning product design through front-end delivery on React, TypeScript, Tailwind and Supabase.
Mario G.
Last position:
Software and Data Engineer at Plexify GmbH
- Architecture, design and development of an MVP application for a provider of specialized travel experiences
- Technologies: Python, FastAPI, Firestore, Firebase, Docker
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Animesh B.
Last position:
Internship Data Science / Data Analytics at Grob Werke GmbH
- Developed, designed, and implemented ETL pipelines for the automatic extraction, transformation, and updating of machine data via REST APIs (Python, OOP)
- Analyzed and processed large volumes of sensor and machine datasets with Python (Pandas, NumPy, SciPy) to identify patterns and time-series-based anomalies in industrial system data
- Developed and implemented machine learning methods (Clustering, XGBoost, Regression) for data-driven time series analysis of technical processes and predictive quality analysis
- Ensured data quality through validation, plausibility checks, and error analyses
- Prepared and presented analysis results for non-technical stakeholders
- Contributed to interdisciplinary IT and data projects in close cooperation with developers, project management, and specialist departments
- Collaborated with interdisciplinary teams and applied software engineering best practices (Git, code reviews, Agile/ Scrum methods, version control)
Rutger B.
Last position:
Partner & Managing Director at AI.IMPACT
- Building an AI & Data Consultancy Practice with the goal of helping European companies adopt Artificial Intelligence and modern data platforms
- End-to-end further development of a production system using modified coding agents (OpenCode). Tech stack: Kubernetes, Argo, Keycloak, Typescript, Grafana, GitOps, DevOps, Playwright
- Internal research project on the use of coding agents in the field of mathematical logic for creating formal models. Use of Cursor IDE and Codex, Codex CLI. Architecture design, quality control and refactoring, as well as writing code and tests. Repository (open source) available pre-launch
- Research on the role of mathematical logic as a formal language that connects IT and AI with business processes
- Project lead for collecting and deploying parking recommendations for rail vehicles with significant savings potential based on real-time data in a mobility and transport company
- Project lead for collecting and distributing process measurement points for real-time control in a mobility and transport company
- Deputy application owner for an app used for communication in the dispatching and provision of rail vehicles
Mugisha E.
Last position:
Freelancer Business Data Analyst at Study Boundless
- Manage WordPress websites, ensuring SEO-friendly structures and high-performance functionality
- Create and optimize Google Ads campaigns, leveraging data to enhance conversion rates and ROI
- Develop Looker Studio & Power BI dashboards to track customer behavior, sales trends, and digital performance
- Implement Google Tag Manager (GTM) and Google Analytics (GA4) to enable precise event tracking and reporting
Christian S.
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Yenal Y.
Last position:
Shiny R Development at RadixITS Gbr
- Implementation and mapping of regulatory requirements (CSRD, ESRS) in Shiny dashboards and backend functions
- Design and development of data-driven modules in Shiny incl. ESG metrics and reporting templates
- Integration of data sources (SQL, SAP HANA, APIs, CSV/Excel) via DBI, odbc, httr and readr
- Creation of interactive visualizations with plotly, highcharter and DT (DataTables)
Sebastian D.
Last position:
Data Scientist at CLADE GmbH
- Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
- Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
- Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution
Deepak R.
Last position:
Machine Learning Engineer at go AVA GmbH
- Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
- Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
- Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.
Discover over 15,000 top freelancers
Statistics of experts using Plotly
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
2 years
Positions per freelancer
7
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
90%
Doctorate
20%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
98%
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 Germany 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 Germany using Plotly
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.
Plotly 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 (69%)
- Education (49%)
- Manufacturing (33%)
- Professional Services (33%)
- Banking and Finance (31%)
- Healthcare (31%)
- Automotive (22%)
- Insurance (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Interactive data visuals
Plotly is a charting library for creating interactive, browser-based visualizations from structured data. It supports Python, R, Julia and JavaScript, with features such as zooming, filtering, hover details and export options. Companies use it to turn complex analysis into clear tools for business and technical users.
Dash applications
Plotly Dash extends the visualization layer into complete analytical web applications. Specialists combine charts, controls, tables and callbacks to create interfaces for monitoring, exploration and reporting without building a separate frontend stack. Dash projects often use Python data processing, responsive layouts and reusable components.
Ecosystem and tooling
Plotly work commonly connects to pandas, NumPy, SciPy, scikit-learn and database clients. Strong specialists also understand Jupyter, Streamlit comparisons, JavaScript integration and deployment with containers or cloud services. They choose graph types carefully, manage large datasets and keep interactive behavior maintainable.
- Build exploratory analysis notebooks and reusable chart components
- Connect dashboards to APIs, SQL databases or operational data
- Add filters, drill-downs, callbacks and role-specific views
- Prepare dashboards for deployment, testing and handover
When companies need specialists
Companies bring in freelance Plotly expertise when static reports no longer answer changing questions or when an internal team needs a focused analytics interface. Typical work includes operational monitoring, financial analysis, scientific research, logistics planning and customer-facing data products. In Germany, specialists may also support distributed teams that need clear documentation and reliable remote collaboration.
- Replace manual spreadsheet reporting with interactive analysis
- Turn a prototype into a production-ready Dash application
- Improve slow charts, unclear layouts or fragile callbacks
- Establish visual standards for a broader analytics product
What strong professionals deliver
Quality is more than attractive charts. Experienced professionals define the audience and decision behind each view, validate data transformations, select honest visual encodings and design for accessibility. They separate presentation logic from data logic, handle empty and error states, test interactions and explain deployment requirements clearly.
Choosing the right fit
Look for work samples that show interactive behavior, not only screenshots. Ask how the specialist handled data refreshes, performance, permissions, testing and changes in stakeholder requirements. The right professional can discuss Plotly Express and graph objects, explain when Dash is appropriate, and collaborate with data, product and domain teams in English or German as the project requires.
Frequently asked questions
Curious about Plotly? Here are the answers that come up again and again.
Plotly is used to create interactive charts, maps, statistical visualizations and analytical dashboards. Companies use it in notebooks, internal reporting tools and browser-based applications where users need to inspect data rather than view a fixed image.
Plotly provides built-in interactivity and browser-ready output, while Matplotlib offers extensive control for static figures and Seaborn simplifies statistical charts within the Python ecosystem. The right choice depends on the audience, interaction needs, visual control and deployment context.
A strong Plotly specialist usually understands Python data work with pandas and NumPy, SQL, API integration and data validation. For Dash projects, knowledge of HTML, CSS, callbacks, application structure and deployment is also valuable.
The Plotly experience required depends on the deliverable. A focused charting task may need visualization expertise, while a production Dash application calls for stronger skills in data modeling, state handling, testing, performance and deployment.
Plotly projects are often well suited to remote collaboration because charts, notebooks and applications can be reviewed online. Teams should agree on data access, development environments, documentation and whether German-language communication is needed for users or stakeholders.
Plotly Dash is a practical choice when a Python-focused team needs an analytical interface with charts, controls and data callbacks. A separate frontend may be better when the product needs highly customized interaction, broad consumer-facing behavior or an existing JavaScript design system.
Review a Plotly specialist’s examples for readable layouts, useful interactions and consistent behavior across realistic data conditions. Ask about accessibility, loading speed, empty states, testing and how the visualization supports a specific decision.
A Plotly freelancer should clarify the data sources, refresh schedule, target users, required interactions and hosting environment. It is also important to confirm ownership of data transformations, acceptance criteria, security constraints and how the finished dashboard will be maintained.
The average hourly rate of freelancers in Germany who have used Plotly in their recent projects is 94 €, which corresponds to a daily rate of about 753 € based on an 8-hour working day.
Of the freelancers in Germany who have used Plotly in their recent projects, 100% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used Plotly in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Germany who have used Plotly in their recent projects are German (100%), English (98%), and French (24%).
The most common industries among freelancers in Germany who have used Plotly in their recent projects are Information Technology (69%), Education (49%), and Manufacturing (33%).
The most common business areas among freelancers in Germany who have used Plotly in their recent projects are Information Technology (90%), Business Intelligence (80%), and Product Development (76%).
Main locations of FRATCH Experts, who have recently used Plotly
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 Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
