Data Science Experts in Zurich
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Meet FRATCH Experts in Zurich, who have recently used Data Science
Marco S.
Last position:
Business Analyst, Data Warehouse Developer at NRW.Bank
Business analysis for risk controlling.
Development of a data warehouse based on MS SQL Server with data from the FIS Cross-Asset Trading and Risk Platform (formerly Front Arena).
Implementation of ETL and transformation logic with T-SQL and Python (Jinja2 template engine).
Modeling and automation of data structures with Data Vault.
Creation of reporting and analysis reports with Microsoft Power BI, including user training and implementation.
Gwang Jin K.
Last position:
Data Scientist / Applied AI, Automation & Data Systems Researcher at Independent
- Built and explored applied GenAI, RAG, GraphRAG, local LLM, agentic AI and document-intelligence prototypes for structured analysis, evidence extraction, semantic search, technical reasoning and decision-useful reporting
- Developed private local-LLM workflows and AI system patterns focused on privacy, reproducibility, reviewability, low-cost inference and practical user control
- Built reproducible Python/R workflows for data analysis, automation, API-driven tooling, validation logic, technical documentation and AI-assisted software development
- Designed workflows around explicit assumptions, traceable inputs, reviewable outputs and failure-mode awareness rather than black-box “looks good” demonstrations
- Supported RAHN AG in a chemical/regulatory environment with data extraction and processing around WERCS, a regulatory application for chemical product and compliance data
- Explored complex application/database schemas and wrote nested SQL queries to extract information for mixture calculations, component relationships, regulatory rules and reporting logic
- Continued hands-on development in Git/GitHub/GitLab/Bitbucket, Docker/Linux deployment patterns, REST/API workflows, error handling, technical writing and fast AI-assisted prototyping
- Built technical writing and documentation workflows that turn complex systems into clear runbooks, checklists, decision notes and user-facing explanations
Fabian K.
Last position:
Lecturer at HWZ University of Applied Sciences
- Co-teach in CAS AI Management and CAS AI Innovation programs for future AI managers
- Cover topics including data platforms, AI architecture, technology adoption foundations, and factors influencing enterprise AI initiative success
Oliver S.
Last position:
Managing Director and Employed Actuarial Consultant at ARCS Actuarial and Risk Consulting Stoll GmbH
- Advising insurance companies on modeling, reporting, valuations, risk management and acting as the responsible actuary
- 2023–2025: Improving Solvency II software, annual financial statements (HGB, SII), process optimizations, pricing, SQL and Python-based reports for AXA Germany, Cologne
- 2022–2023: Support for portfolio transfer (RAFM optimization; impact on HGB balance sheet, SII, reinsurance; migration to msg.Life Factory) for Zurich Germany, Cologne
- 2021 and 2024: Redesign of Solvency II model, implementation in Python and model extensions; testing and documentation for PK Rück, Zurich
- 2021–2023: Control calculations for maturity benefits of contracts; specification and testing of policy administration system for product launch at Youplus, Liechtenstein
- 2021: a) Support in preparing a reinsurance treaty: project management and analyses; b) assistance with Solvency II reporting and multi-year planning for Allianz Leben, Stuttgart
- 2020–2021: Further development and optimization of models with Risk Agility FM (RAFM), integration into IFRS 17 reporting process using Unify for Allianz Slovakia and Bulgaria
- 2017–2023: Quality improvement of the RAFM model; methodology development; documentation; process optimization in reporting and planning; automation with Excel and Oracle SQL (on COR-Life); implementation of a life reinsurance treaty; validation of numerical results in the policy administration system for SV Versicherung, Stuttgart and Mannheim
- 2015–2017: Project management and senior actuary in developing an actuarial projection tool and in the IMAP process with the regulator; guiding the reporting team; analyses; methodology development; process design; coordination with IT, asset and risk management for Allianz Leben, Stuttgart
- 2016–2020: Review of cash flow models for investment projects for B Capital, Zurich
- 2015: Redevelopment of MCEV calculation methodology based on MVBS; weakness analysis and collaboration on fixes for Allianz SE, Munich
- 2014: Solvency II risk capital reporting project: validation of data deliveries; analyses for management and regulator; tool development; process and documentation improvements; training of team members for Aegon Group, The Hague, Netherlands
Ala L.
Last position:
VR/AR/ML Project Site Lead (contract by Experis) at Meta
- Acted as project lead in different internal projects, including the development and implementation of innovative solutions based on machine learning, virtual and augmented reality with the aim of providing great user experience
- Drove project planning, execution and reporting, designed risk mitigation and schedule adjustment plans to bring the projects on the green path
- Directed the process optimization and conducted project reviews by being the liaison between engineering teams and executive stakeholders
- Served as agile coach and led the scrum ceremonies such as daily stand-ups, sprint planning, sprint review and sprint retrospective
Karl E.
Last position:
incl. CI/CD, automation at AALS Software AG
- Designed and delivered a practical real-time course on Flink and Hadoop with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, and Kafka
- Gained extensive DevOps and CI/CD experience
- Created ETL/ELT pipelines with Apache tools and Pentaho
- Led projects in municipal software, financial services, and big data with Kafka
- Developed AI/NLP models and chatbots with RASA, Chatter, and Dialogflow
- Built and managed a TypeDB knowledge database
- Worked with OpenStack, Kubernetes, and Podman
Revealed U.
Last position:
Executive Enterprise Data & Analytics Advisor at A medium-size private bank
- Responsible for defining and implementing an enterprise Data Governance framework with an initial focus on BCBS-239, including Data Catalog tooling and formalized data asset ownership.
- Led the turnaround and delivery of an Enterprise Data Warehouse to support regulatory and management reporting on a robust, integrated data platform with a primary focus on Finance.
- Served as a key contributor to the bank’s Data, Analytics & AI strategy, which was subsequently approved by the management board.
Discover over 15,000 top freelancers
Statistics of experts using Data Science
Aggregated from the professional profiles of matched freelancers.
Experience
26 years
Position duration
2.8 years
Positions per freelancer
12
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Banking and Finance, Education, Information Technology
Certification focus areas
Project Management, Business Intelligence, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
67%
Doctorate
17%
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 Zurich 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 Zurich 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.
- Banking and Finance (86%)
- Education (71%)
- Information Technology (71%)
- Professional Services (71%)
- Government and Administration (57%)
- Insurance (43%)
- Aerospace and Defense (29%)
- Manufacturing (29%)
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 decisions from structured and unstructured data. Experts design experiments, prepare datasets, build predictive models and communicate findings clearly. The work can support forecasting, personalization, risk analysis and operational planning.
Typical deliverables
Data Science projects often produce models, analytical products and repeatable workflows rather than one-off reports.
- Demand, sales and capacity forecasts
- Recommendation and classification systems
- Customer segmentation and churn analysis
- Experiment design and impact measurement
- Dashboards, data products and model documentation
Ecosystem and tooling
Python is widely used with pandas, NumPy, scikit-learn, Jupyter and PyTorch, while R remains valuable for statistical analysis and research. SQL is central to querying business data, and Spark supports distributed processing. Strong experts also work with cloud warehouses, dbt, APIs, Git and model deployment tools.
When companies need specialists
Companies bring in freelance expertise when internal teams need a clear analytical direction, a model delivered quickly or an independent review of existing work. In Zurich, projects may involve financial services, insurance, healthcare, retail or industrial operations, with collaboration arranged remotely, on site or in a hybrid format.
- Business questions remain unclear or difficult to measure
- Data quality blocks a product or reporting initiative
- A prototype must become a reliable production service
- Existing models need validation, monitoring or improvement
Skills that matter
Strong professionals connect technical choices to business outcomes. They understand sampling, feature engineering, model selection, validation, explainability and data privacy. They can also translate results for non-technical stakeholders and create pipelines that remain maintainable after handover.
How quality is judged
Look for evidence of sound problem framing, reproducible analysis and honest evaluation rather than impressive model names alone. A capable specialist explains assumptions, handles leakage and bias, tests performance against a meaningful baseline and defines how results will be monitored. Experience with the relevant domain and clear communication are equally important.
Frequently asked questions
What clients ask us most about Data Science — answered in short.
Data Science is used to turn raw data into forecasts, recommendations, classifications and measurable business insights. Typical applications include fraud detection, demand planning, customer analysis, medical research and process optimisation.
Data Science is a broad discipline that combines statistical analysis, software development, domain knowledge and machine learning. Data analytics often focuses on explaining what happened, while machine learning focuses on systems that learn patterns; a Data Science project may include both.
A strong Data Science specialist usually works comfortably with SQL, Python, data modelling, statistics and visualisation. Depending on the project, useful adjacent skills include cloud warehouses, MLOps, Spark, experimentation, product thinking and data governance.
The right level for Data Science depends on the risk, data quality and production scope of the work. A focused analysis may need a different profile than a regulated decision system, a real-time model or a platform that must be maintained by an internal team.
Data Science is often well suited to remote collaboration because data, notebooks and cloud environments can be shared securely. On-site sessions in Zurich can still help with stakeholder interviews, sensitive data access and workshops, while German or English expectations should be agreed at the start.
Good Data Science work starts with a precise question and uses suitable data, transparent assumptions and rigorous validation. Ask how the specialist handles missing data, leakage, bias, baseline comparisons, explainability and monitoring after deployment.
Data Science does not always require machine learning. A well-designed experiment, statistical analysis, forecasting method or clear data product can answer the business question more effectively than a complex model.
Before starting Data Science work, clarify the decision the project must support, available data, access rules, success criteria and who owns the final result. It is also important to agree on deliverables, production responsibilities, documentation and how findings will be communicated.
The average hourly rate of freelancers in Zurich, Switzerland who have used Data Science in their recent projects is 127 €, which corresponds to a daily rate of about 1,017 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Data Science in their recent projects, 100% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Data Science in their recent projects have 26 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Zurich, Switzerland who have used Data Science in their recent projects are German (100%), English (100%), and French (57%).
The most common industries among freelancers in Zurich, Switzerland who have used Data Science in their recent projects are Banking and Finance (86%), Education (71%), and Information Technology (71%).
The most common business areas among freelancers in Zurich, Switzerland who have used Data Science in their recent projects are Business Intelligence (86%), Information Technology (86%), and Product Development (71%).
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.
Countries:
Zurich 
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