Data Science Experts in Austria
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Meet FRATCH Experts in Austria, who have recently used Data Science
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and zero-knowledge encryption, cloud-native on EU infrastructure
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Chrisabel P.
Last position:
AI Systems & Product Strategy Expert at Webmeisterin
I build. I advise. I think in systems. After years leading digital transformation at scale — Accenture, BP, Lidl — I made a deliberate choice: trade platform dependency for structural independence. My focus is at the intersection of AI systems, product strategy, and venture thinking. I work with operators and founders who want to move fast without losing control — of their data, their stack, their direction.
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Shahram F.
Last position:
Senior Product Owner at Elderly Neighbour Watch
Delivered a digital, non-profit neighborhood platform to connect older adults with volunteers for practical support and social companionship. Responsible for business analysis, requirement definition, platform evaluation and delivery of a locally scalable service solution focusing on AI-driven enhancements, data-based optimization and clear market positioning. Evaluated several low-code/business platforms including Odoo and Glide Apps and assessed monday.com as a CRM-like option before selecting Glide Apps for implementation. Implemented a non-native mobile and desktop application with Glide Apps and the web presence with WordPress. Tasks
- Gathering, analyzing and structuring business requirements for the product and service model
- Conducting market, target group and competitor analyses to position the offering
- Evaluating and selecting suitable low-code/business platforms for implementation
- Defining core user journeys, business processes and operational workflows for older adults, volunteers and administration
- Translating business requirements into functional requirements for profiles, task management, coordination, communication and notifications
- Guiding the implementation of the non-native mobile and desktop application in Glide Apps as well as the web presence in WordPress
- Preparing the business side for future AI-powered features such as intelligent matching and data-based optimization
- Ensuring compliance with GDPR requirements Results
- Successfully delivered digital support platform connecting older adults with volunteers
- Solid foundation for market positioning through market, target group and competitor analyses
- Delivered a functional non-native mobile and desktop application with Glide Apps and a supporting web presence with WordPress
- Established foundation for scaling and AI-based further development
Mario T.
Last position:
External Lecturer at FH Kufstein Tirol – University of Applied Sciences
- Study: Data Science & Intelligent Analytics
- Module: Big Data Processing
Nikolaus J.
Last position:
Integration Architect at CECIL
Designed an API-first omnichannel integration, linking WhatsApp Business API with Salesforce Marketing Cloud to unify customer data across multiple platforms.
Automated customer onboarding and engagement workflows using Marketing Cloud Journeys, SSJS, and Azure Functions.
Developed a middleware layer to sync WhatsApp interactions with Sales Cloud for seamless customer experience tracking.
Armin F.
Last position:
Head of AI & Data Science at Ascent DACH
- Lead architect for AI and ML projects including GenAI, LLM-based apps and forecasting solutions
- Guided customers through solution scoping, architecture design, and PoCs across various industries (Pharma, Insurance, Logistics, FMCG)
- Delivered production ML pipelines using Azure ML, MLflow, and MLOps best practices
- Responsible for effort estimation, delivery and staffing of 5 – 10 projects simultaneously
- Hiring manager for the data science and AI team and responsible for creating the technological offering and roadmap in the AI & Data Science space
- Built and scaled the AI/Data Science service offering from scratch to a high 6-figure annual revenue with 30+ successful deliveries and 20+ clients
- Regular speaker at AI and data science conferences and academic institutions
Christian S.
Last position:
Commercial Manager/CFO at MEV Independent Railway Services GmbH
- CFO, authorized signatory, commercial managing director: finance, HR, IT & organization, funding
- Achieved reorganization and 100% growth in staff and revenue over four years while keeping fixed costs constant
- Improved a negative equity ratio to plus 30%
- Implemented a modern IT and reporting system
Rene S.
Last position:
Head of Digital Services & IT at reet systems gmbh / THEOPHIL Holding GmbH
- Overall responsibility for IT, software development, and digital services of the company for brands such as Rosenberger, Rosehill, Burger King Austria (approx. 70 companies)
- Built the holding's lakehouse and data analytics platform
- Established and led the software development and IT department
- Established and led the operation (cloud-native AWS) of the B2B platform
- Connected IoT systems and developed models for predictive maintenance and production planning, data lake/lakehouse, and BI
- Preparation for ISO 27001 information security certification
- Technologies: Cloud, AWS, Java, Cypress, Angular, Python, Go
Kevin L.
Last position:
Data Consultant at VBV Pension and Provident Fund Austria
Development of a structured framework and comprehensive guidelines for documenting business and audit processes in a regulated financial environment. Support for the standardization of process documentation to improve transparency, consistency, and traceability across all operational workflows. Contribution to defining documentation standards, templates, and governance principles for internal process management and audit readiness.
Discover over 15,000 top freelancers
Statistics of experts using Data Science
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
3 years
Positions per freelancer
12
Top business areas
Information Technology, Business Intelligence, Operations
Top industries
Information Technology, Manufacturing, Retail
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
90%
Certifications per freelancer
5
Most common languages
German, English, French
Speak two or more languages
100%
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 Austria 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 Austria 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%)
- Manufacturing (64%)
- Retail (64%)
- Banking and Finance (55%)
- Education (45%)
- Healthcare (45%)
- Professional Services (45%)
- Media and Entertainment (36%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Data Science covers
Data Science combines statistics, programming, domain knowledge and machine learning to extract useful evidence from structured and unstructured data. It supports forecasting, classification, experimentation, anomaly detection and decision automation. The work can range from an exploratory analysis to a production service used by customers or internal teams.
Typical applications
Data Science is used wherever organisations need better predictions or clearer decisions. Common deliverables include:
- Demand, sales and capacity forecasts
- Customer segmentation and churn analysis
- Recommendation and ranking systems
- Fraud, risk and anomaly detection
- Experiment analysis and pricing models
Ecosystem and tooling
Python is central to many projects, with pandas, NumPy, SciPy and scikit-learn supporting analysis and modelling. Specialists may also use PyTorch or TensorFlow for deep learning, SQL and Spark for data processing, and notebooks for exploration. Production work often connects models to APIs, containers, cloud services, feature stores and monitoring workflows.
When companies need specialists
Companies often bring in freelance expertise when data is scattered, a model must move from a notebook into production, or an internal team needs focused support. A specialist can clarify the business question, assess data quality and define a measurable path to delivery. Austria-based organisations may also value professionals who can collaborate on site while keeping remote work efficient across distributed teams.
- A new data product needs a sound modelling approach
- Existing predictions are hard to explain or maintain
- Pipelines, experiments or model monitoring need structure
- A team needs practical support with cloud deployment
Skills that make a difference
Strong professionals connect technical choices to business outcomes. They know how to prevent leakage, select meaningful evaluation methods, handle imbalanced data and communicate uncertainty without overstating a result. They also understand data engineering, software development, visualisation, MLOps and responsible use of sensitive information.
Assessing project fit and quality
Start with the decision the project should improve, the data available and the way success will be evaluated. Ask for a clear approach to validation, documentation, reproducibility and deployment rather than focusing only on model sophistication. Quality work leaves behind understandable assumptions, tested pipelines and a practical plan for maintenance, retraining and stakeholder review.
Frequently asked questions
What clients ask us most about Data Science — answered in short.
Data Science helps companies turn data into forecasts, classifications, recommendations and decisions. Typical work includes demand planning, customer analysis, fraud detection, process optimisation and experimentation. The right approach depends on the business question, data quality and how the result will be used.
Data Science commonly extends analytics from describing past performance to predicting outcomes or supporting automated decisions. Business intelligence often focuses on reports, dashboards and established metrics, while analytics can cover a broader range of diagnostic work. The boundaries overlap, so a project may need strong reporting skills as well as modelling expertise.
Data Science projects benefit from SQL, data engineering, statistics, visualisation and software development skills. Experience with cloud infrastructure, APIs, containers, MLOps and model monitoring is valuable when a model must run reliably in production. Clear communication is equally important for explaining assumptions and uncertainty.
Data Science work does not require the same depth for every assignment. A focused exploratory analysis may suit a specialist with experience in a similar dataset, while production machine learning requires evidence of deployment, testing, monitoring and maintenance. Judge fit by the project scope, data complexity and operational risk rather than by a title alone.
Data Science is often suitable for remote collaboration because data preparation, modelling and documentation can be managed through shared development and cloud environments. On-site work can help with workshops, access controls or close stakeholder alignment. Agree early on data access, communication routines, language expectations and any restrictions on handling sensitive information.
Data Science quality is shown by a clear problem definition, sound validation and a transparent explanation of limitations. Ask how the specialist handles missing data, leakage, bias, reproducibility and model drift. Strong professionals connect the technical result to a decision, measurable outcome and realistic maintenance plan.
Data Science should favour the simplest method that meets the decision need and can be validated properly. Machine learning may help when relationships are complex or the data is high-dimensional, while a statistical model can be easier to explain and maintain. The choice should reflect data volume, prediction value, interpretability and operational constraints.
Data Science specialists should clarify the business objective, available data, decision owner and definition of success before selecting tools or models. They should also confirm access permissions, delivery expectations, deployment ownership and how results will be reviewed. This prevents an attractive analysis from becoming a solution that nobody can use.
The average hourly rate of freelancers in Austria who have used Data Science in their recent projects is 115 €, which corresponds to a daily rate of about 920 € based on an 8-hour working day.
Of the freelancers in Austria who have used Data Science in their recent projects, 100% hold at least a Bachelor's degree and 90% hold at least a Master's degree.
On average, freelancers in Austria who have used Data Science in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Austria who have used Data Science in their recent projects are German (100%), English (100%), and French (36%).
The most common industries among freelancers in Austria who have used Data Science in their recent projects are Information Technology (91%), Manufacturing (64%), and Retail (64%).
The most common business areas among freelancers in Austria who have used Data Science in their recent projects are Information Technology (100%), Business Intelligence (91%), and Operations (73%).
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.
Vienna Graz 
Vienna Countries:
- Germany
- Austria
- Switzerland
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FRATCH CEO
