Data Science Experts in Austria
for smarter models, insights and decisions with vetted freelancers matched in minutesHire experts who turn complex data into reliable forecasts, machine learning models and decision-ready dashboards. Work with specialists across Python, SQL, R, cloud data platforms and model deployment, matched quickly with vetted, available freelancers.
Meet FRATCH Experts in Austria, who have recently used Data Science
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own company for AI governance and data products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact assessment 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
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of Master's theses and participation on examination boards
- Industry talks for businesses 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
- Integration with bank account systems using SEPA direct debit mandates and message formats under ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in live operations
- Own applications under my own brand: shared codebase, separate deployment for each target device
- AI-assisted software development (vibe-coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Professional publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Professional 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
Delivery of a digital, non-profit neighborhood platform connecting older people with volunteers for practical support and social companionship. Responsibility for business analysis, requirements definition, platform evaluation and delivery of a locally scalable service solution, with a focus on AI-supported development, data-driven optimization and clear market positioning. Evaluation of several low-code/business platforms, including Odoo and Glide Apps, as well as assessment of monday.com as a CRM-related option, before selecting Glide Apps for implementation. Implementation of a non-native mobile and desktop application with Glide Apps and the website with WordPress. Responsibilities
- 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 key user journeys, business processes and operational workflows for older people, volunteers and Administration
- Translating business requirements into requirements for profiles, task management, coordination, communication and notifications
- Supporting the implementation of the non-native mobile and desktop application in Glide Apps and the website in WordPress
- Preparing future AI-supported functions such as intelligent matching and data-driven optimization
- Ensuring compliance with GDPR requirements Results
- Successfully delivered digital support platform connecting older people with volunteers
- Solid foundation for market positioning through market, target group and competitor analyses
- Implemented functional non-native mobile and desktop application with Glide Apps and accompanying website with WordPress
- Created foundation for scaling and AI-based further development
Laura H.
Last position:
Sr. BI Consultant & Project Manager at Multivariata
- Project Lead, Sr. BI Expert, Data Scientist, Automation Engineer for Marketing Analytics | Customer Insights (retail, e-commerce)
- Product Manager: Asset Metadata Ingest & Provide
- Product Owner Business Intelligence – responsible for 2 teams: cloud data engineering, data warehouse (logistics)
- Product Owner for setting up a reporting platform on Azure & Power BI (finance)
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.
Kevin L.
Last position:
Data Consultant at VBV Pensions- und Vorsorgekasse Österreich
Development of a structured framework and comprehensive guidelines for documenting business and audit processes in a regulated financial environment. Support in standardizing 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.
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
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
3.2 years
Positions per freelancer
13
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Retail, Banking and Finance
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
82%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
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 Austria 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 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 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 (92%)
- Retail (67%)
- Banking and Finance (58%)
- Manufacturing (58%)
- Professional Services (58%)
- Healthcare (50%)
- Education (42%)
- Media and Entertainment (42%)
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 communication to turn raw information into useful evidence. Specialists clean and explore data, identify patterns, test hypotheses and build models that support decisions. Their work can improve forecasting, customer analysis, risk assessment, operations and product development.
Typical project outcomes
Data Science work connects business questions with measurable analytical deliverables:
- Predictive models for demand, churn, fraud or maintenance
- Segmentation, recommendation and propensity systems
- Experiments, causal analysis and forecasting
- Dashboards, data products and decision workflows
A strong project starts with a clear outcome, reliable data access and a practical plan for using the results in daily operations.
Ecosystem and tooling
Professionals commonly work with Python, R and SQL, using pandas, NumPy, scikit-learn, TensorFlow or PyTorch for analysis and modelling. Spark and Databricks support larger workloads, while cloud services provide storage, notebooks and deployment options. Good practice also includes Git, testing, documentation and reproducible environments.
When companies need specialists
Companies often bring in freelance expertise when internal teams lack capacity, a model must move from research into production or a new data source needs assessment. This is useful for a focused discovery phase, a defined model build, validation of an existing approach or knowledge transfer to a permanent team. In Austria, remote work can suit data projects, while on-site workshops may help with domain alignment and stakeholder decisions.
Skills that make a difference
Strong specialists understand both the mathematics and the business context. They choose an appropriate method rather than forcing every problem into machine learning, explain uncertainty clearly and check for bias, leakage and weak data quality. They can also communicate with product, engineering, compliance and leadership teams, then document assumptions so others can maintain the result.
Evaluating Data Science work
Quality is visible in more than model accuracy. Ask how the data was collected, which baseline was used, how performance was validated and what happens when inputs change. A dependable professional defines acceptance criteria, creates a reproducible workflow and connects the model to a decision or process. Clear handover materials, monitoring plans and sensible limits are signs that the work is ready for real use.
Frequently asked questions
What clients ask us most about Data Science — answered in short.
Companies use Data Science to understand behaviour, forecast demand, detect anomalies and improve decisions with evidence. Typical work includes customer segmentation, recommendation systems, risk models, process optimisation and experiments. The right use case depends on data quality and the decision the result must support.
Data Science often includes predictive modelling, experimentation and advanced statistical methods, while business intelligence usually focuses on reporting what has happened. Data analytics can cover both descriptive and diagnostic work, so the boundaries overlap. A project may need all three, with different specialists contributing at different stages.
A capable Data Science specialist should usually work comfortably with SQL, data preparation, statistics and visualisation. Familiarity with Python or R, cloud storage, version control and model deployment is also valuable. Strong communication and domain understanding help turn an analysis into a result people can use.
The required experience depends on the work, not just the technology. A focused analysis may need a specialist who can explore data and communicate findings, while a production model requires experience with validation, deployment, monitoring and operational ownership. Ask candidates to show comparable deliverables and explain the decisions behind them.
Yes, much Data Science work can be handled remotely when data access, documentation and communication are well organised. Workshops with Austrian stakeholders may still benefit from on-site collaboration, especially during discovery or requirements definition. Access controls and secure environments should be agreed before work begins.
A good Data Science specialist compares machine learning with rules, statistical models and straightforward reporting before choosing an approach. A simpler method may be easier to explain, maintain and validate, while a more complex model can help when relationships are nonlinear or inputs are numerous. The choice should follow the business cost of errors and the available data.
Evaluate Data Science work through clear problem framing, transparent data preparation and validation against a relevant baseline. Ask how leakage, bias, missing values and changing inputs were handled. A quality deliverable includes reproducible analysis, understandable conclusions and a practical plan for deployment or continued use.
Before starting Data Science work, clarify the decision to improve, the available data, ownership of sources and the expected deliverable. Confirm access, privacy constraints, review points and how success will be assessed. It is also important to agree whether the work ends with an analysis, a model, a deployed service or team handover.
The average hourly rate of freelancers in Austria who have used Data Science in their recent projects is 114 €, which corresponds to a daily rate of about 909 € 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 82% 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 3.2 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 (33%).
The most common industries among freelancers in Austria who have used Data Science in their recent projects are Information Technology (92%), Retail (67%), and Banking and Finance (58%).
The most common business areas among freelancers in Austria who have used Data Science in their recent projects are Information Technology (100%), Business Intelligence (92%), and Product Development (75%).
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
