Data Science Experts in Nuremberg
to turn complex data into decisions, matched in minutes with vetted, available freelancersHire experts who turn raw data into reliable models, forecasts and decision tools using Python, SQL, machine learning and modern cloud platforms. FRATCH precisely matches you with vetted, available freelancers who fit your project and can start quickly.
Meet FRATCH Experts in Nuremberg, who have recently used Data Science
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.
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.
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
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.
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
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
1.5 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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Nuremberg 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 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 19 Sep 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 covers
Data Science combines statistics, programming and domain knowledge to extract useful signals from structured and unstructured data. It supports forecasting, recommendation systems, anomaly detection, customer segmentation and automated decision support. Strong work connects analytical findings to a clear business action.
Where it is used
Companies apply Data Science across manufacturing, logistics, retail, healthcare, finance and software products. Typical deliverables include:
- Demand and capacity forecasts
- Predictive maintenance and quality models
- Recommendation and personalization systems
- Customer, risk and fraud analytics
- Dashboards with actionable metrics
Ecosystem and tooling
Python is central for data preparation, experimentation and model development, with pandas, NumPy, scikit-learn and specialized libraries supporting the workflow. SQL remains essential for working with operational data, while Jupyter, Git and testing practices make analysis reproducible. Cloud services, Spark, dbt and orchestration tools extend Data Science into production environments.
When companies need specialists
Freelance expertise is useful when internal teams have valuable data but lack a clear route from question to dependable output. It can also help when a prototype must become a monitored service, when data pipelines need improvement or when an existing model produces inconsistent results. In Nuremberg, remote collaboration can support local industrial and service businesses, while on-site workshops may help with domain discovery.
What strong professionals deliver
Experienced professionals clarify the decision behind the model before choosing an approach. They assess data quality, define suitable evaluation methods and explain uncertainty in language stakeholders can use. They also document assumptions, protect sensitive information and design handover processes so teams can maintain the result after delivery.
Skills beyond modeling
Effective Data Science work reaches across data engineering, analytics engineering, software development and product delivery. Look for specialists who can build reliable SQL transformations, communicate findings visually and integrate models through APIs or batch workflows. Knowledge of machine learning operations, experiment tracking and cloud security is valuable when a solution must run continuously rather than remain a notebook exercise.
Frequently asked questions
Curious about Data Science? Here are the answers that come up again and again.
Data Science is used to find patterns in business data and turn them into forecasts, recommendations or automated decisions. Common applications include demand planning, fraud detection, predictive maintenance, customer analysis and quality control.
Data Science is a broader practice that can include data analysis, statistical modeling, machine learning, data preparation and deployment. Data analytics often focuses on explaining past and current performance, while machine learning is a set of methods that Data Science may use to make predictions.
A strong Data Science specialist often combines Python, SQL, statistics and data visualization with knowledge of cloud services or data engineering. For production work, experience with APIs, workflow orchestration, model monitoring and version control is especially useful.
The right level of Data Science experience depends on the goal, data quality and consequences of the decisions involved. A focused analysis may need a specialist who can work independently from exploration to recommendation, while a production model calls for experience with deployment, monitoring and handover.
Data Science is often well suited to remote collaboration because data exploration, coding and documentation can be handled online. Workshops with teams in Nuremberg may still be useful for understanding operational processes, access constraints and the context behind the data.
Before starting Data Science work, define permitted data access, ownership, retention and security requirements. A capable specialist should be comfortable using controlled environments, minimizing sensitive data and documenting how datasets and results are handled.
Good Data Science work has a clear business question, traceable data preparation and evaluation that reflects real use. Ask the specialist to explain assumptions, error patterns, limitations and how the result will be monitored after delivery rather than focusing only on a model score.
A typical Data Science workflow may use Python with pandas, NumPy and scikit-learn, SQL for data access, and Jupyter for exploration. Git, cloud storage, Spark, dbt, experiment tracking and orchestration tools become important when analyses must be reproducible and models must run reliably in production.
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 1.5 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.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf Countries:
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
