
Data Science Experts in Nuremberg
matched in minutes with vetted, available freelancersHire experts who turn complex data into reliable forecasts, recommendation systems and measurable business insight with Python, R, SQL and machine learning. FRATCH connects you with precise matches from vetted, available freelancers who can join remotely or collaborate on site in Nuremberg.
Meet FRATCH Experts in Nuremberg, who have recently used Data Science
Muntaha S.
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
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
Arun Sai T.
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
Puranjan B.
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Felix K.
Last position:
Senior Data Scientist at Novartis Pharma AG
- Led the implementation and validation of digital technology for real-world mobility assessment across clinical trials with high compliance rates
- Headed a cross-functional initiative that bridged clinical and data science teams, aligning digital technology integration with strategic goals
- Innovated algorithm development for sensor data analysis, leveraging time-series data to extract novel insights and improve predictive accuracy
- Designed and deployed predictive models using machine learning, integrating digital device data with clinical datasets
- Delivered strategic insights through the creation of dashboards and reports, ensuring effective data quality control and visualization for clinical trials
- Ensured reproducibility in data science workflows by establishing robust coding practices, comprehensive documentation, and version control
- Co-led exploratory statistical plans for digital biomarkers, advancing understanding and application of digital technologies in healthcare research
- Advised clinical teams as a subject-matter expert on digital technologies, fostering strategic integration and effective utilization in clinical trials
- Managed stakeholder engagement by collaborating with internal and external stakeholders, including contract research organizations and technology vendors
- Influenced decision-making by presenting analyses and findings to various management levels and stakeholders, emphasizing actionable insights and implications
- Disseminated knowledge through peer-reviewed publications
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Ashmi J.
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
Vasuraj B.
Last position:
Cloud Data Analyst at Bhatia Reply
- Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
- Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
- Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
- Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
- Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker
Ekaansh K.
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Anshul P.
Last position:
BSc Artificial Intelligence at Friedrich Alexander University Erlangen-Nuremberg
- Current Grade: 1.4.
- Applied Programming on Signal Processing: Fourier Transform, Filtering, VisPy, and NumPy.
- FAUST WebSecurity Workshop.
- Hands-on experience in LLMs, Applied Data Science, and data analysis.
- Proficient in Python, PyQt5, C++, MS Office, Git, Pandas, NumPy, VisPy, JavaScript, CSS, Machine Learning, and HTML.
Maria E.
Last position:
Certificate of Further Education in Data Science at IU Akademie (IU Internationale Hochschule GmbH)
- One module corresponds to a workload of 150 teaching units.
Discover over 15,000 top freelancers
Statistics of experts using Data Science
Aggregated from the professional profiles of matched freelancers.
Experience
8 years (Germany: 14 years)

Position duration
1.6 years (Germany: 2.2 years)

Positions per freelancer
5 (Germany: 9)

Top business areas
Information Technology, Product Development, Research and Development

Top industries
Information Technology, Education, Automotive

Certification focus areas
Research and Development, Business Intelligence, Information Technology
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
80%
Doctorate
10% (Germany: 22%)

Certifications per freelancer
2 (Germany: 3)

Most common languages
English, German, Hindi

Speak two or more languages
100% (Germany: 97%)
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 Nuremberg 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 Nuremberg using Data Science
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.
Data Science 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 (91%)
- Education (55%)
- Automotive (36%)
- Manufacturing (36%)
- Healthcare (27%)
- Professional Services (27%)
- Banking and Finance (18%)
- Pharmaceutical (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Science delivers
Data Science combines statistics, programming, domain knowledge and machine learning to turn raw data into useful decisions. Experts build predictive models, forecasting tools, recommendation engines, anomaly detection and analytical products. They work with structured tables, text, images, sensor readings and event streams.
Methods and models
A strong practice starts with a clear business question and trustworthy data. Specialists select suitable methods, test assumptions and measure results against a meaningful baseline. Their work can include regression, classification, clustering, time-series forecasting, natural language processing and deep learning, with careful attention to bias, leakage and uncertainty.
Ecosystem and tooling
Python is common for notebooks, data preparation and model development, while R remains valuable for statistical analysis and reporting. SQL, pandas, NumPy, scikit-learn, PyTorch and TensorFlow often form the technical stack. Cloud warehouses, Spark, dbt, MLflow, Docker and orchestration tools help teams manage larger workloads and repeatable model delivery.
Where companies use it
Data Science supports decisions and products across manufacturing, logistics, healthcare, finance, retail and energy. Typical assignments include:
- Predicting demand, maintenance needs or customer churn
- Segmenting customers and detecting unusual activity
- Ranking recommendations, search results or operational priorities
- Extracting meaning from documents, language or images
When to bring in a specialist
Companies often need freelance expertise when they have valuable data but lack a clear modeling path, or when an internal team needs support for a critical delivery. A specialist can audit data quality, define an evaluation plan, create a proof of concept, productionize a model or transfer working practices to the team. In Nuremberg, remote collaboration can be combined with on-site workshops when domain knowledge and stakeholder alignment matter.
What distinguishes strong professionals
The best professionals connect model quality with business impact. They explain trade-offs clearly, build reproducible pipelines and document data sources, features, assumptions and limitations. Look for evidence of production monitoring, version control, privacy-aware handling and communication with non-technical stakeholders. Strong specialists also know when a simple statistical method is safer and more useful than a complex model.
Frequently asked questions
Curious about Data Science? Here are the answers that come up again and again.
Data Science helps companies discover patterns, predict future events and automate decisions from data. Typical outcomes include demand forecasts, fraud detection, customer segmentation, recommendations, document analysis and predictive maintenance.
Data Science often focuses on prediction, experimentation and machine learning, while business intelligence mainly explains what has already happened through reports and dashboards. Data analytics can cover both areas, so the boundaries depend on the project and the team’s responsibilities.
A capable Data Science specialist usually combines statistics with Python or R, SQL, data visualization and sound software practices. Experience with cloud data warehouses, machine learning operations, domain research and stakeholder communication can be just as important as model selection.
The right Data Science experience depends on the problem, data maturity and delivery target. A focused analysis may need a specialist who can work independently with messy data, while a production model also calls for deployment, monitoring, security and collaboration with product or software teams.
Data Science work is often well suited to remote collaboration because data access, notebooks, repositories and experiments can be shared securely. Teams in Nuremberg should define communication routines, access controls and documentation standards, adding on-site workshops when business context or German-language stakeholder work benefits from direct contact.
Before a Data Science engagement begins, define permitted data use, access boundaries, ownership and retention rules. The specialist should be able to explain how sensitive information is protected, how experiments are documented and how outputs will be reviewed before they influence real decisions.
A strong Data Science professional can describe the business question, data limitations, baseline, evaluation method and operational risks behind past work. Ask how they handled missing values, leakage, changing data, model interpretation and failure cases rather than judging quality by model complexity alone.
Data Science is not automatically the best answer when a clear report, rule or statistical test solves the problem. A thoughtful specialist first checks whether the data supports the goal and recommends the simplest approach that is accurate, maintainable and useful to the people making decisions.
The average hourly rate of freelancers in Nuremberg, Germany who have used Data Science in their recent projects is 57 €, which corresponds to a daily rate of about 456 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Data Science in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Nuremberg, Germany who have used Data Science in their recent projects have 8 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Nuremberg, Germany who have used Data Science in their recent projects are English (100%), German (91%), and Hindi (36%).
The most common industries among freelancers in Nuremberg, Germany who have used Data Science in their recent projects are Information Technology (91%), Education (55%), and Automotive (36%).
The most common business areas among freelancers in Nuremberg, Germany who have used Data Science in their recent projects are Information Technology (82%), Product Development (82%), and Research and Development (82%).
Main locations of FRATCH Experts, who have recently used Data Science
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.
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