Data Science Experts in Zurich
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Meet FRATCH Experts in Zurich, who have recently used Data Science
Marco S.
Last position:
MS Power BI Developer at Bayern Facility Management GmbH
Power BI development and administration, training, consulting, data modeling, DAX development
MS Fabric, MS Power BI, DAX
Oliver S.
Last position:
Managing Director and employed actuarial consultant at ARCS Actuarial and Risk Consulting Stoll GmbH
- Advising insurance companies on modelling, reporting, valuations, risk management and acting as Appointed Actuary
- 2023–2025: Improvement of Solvency II software, annual financial statements (HGB, SII), process optimisation, pricing, SQL- and Python-based reports for AXA Deutschland, Cologne
- 2022–2023: Support with portfolio transfer (RAFM optimisation; impact on HGB balance sheet, SII, reinsurance; migration to msg.Life Factory) for Zurich Deutschland, Cologne
- 2021 and 2024: Redesign of Solvency II model, implementation in Python and model enhancements; testing and documentation for PK Rück, Zurich
- 2021–2023: Control calculations for policy maturity benefits; specification and testing of an administration system for product launch at Youplus, Liechtenstein
- 2021: a) Support in drafting a reinsurance contract: project management and analyses; b) Support with Solvency II reporting and multi-year planning for Allianz Leben, Stuttgart
- 2020–2021: Further development and optimisation of models with Risk Agility FM (RAFM), integration into the IFRS 17 reporting process with Unify for Allianz Slovakia and Bulgaria
- 2017–2023: Quality improvement of the RAFM model; methodology development; documentation; optimisation of reporting and planning processes; automation with Excel and Oracle SQL (on COR-Life); implementation of a life reinsurance contract; validation of mathematical figures in the administration system for SV Versicherung, Stuttgart and Mannheim
- 2015–2017: Project management and senior actuary in the development of an actuarial projection tool and in the IMAP process with the regulator; guidance of the reporting team; analyses; methodology development; process design; coordination with IT, asset management and risk management for Allianz Leben, Stuttgart
- 2016–2020: Review of cash flow models for investment projects for B Capital, Zurich
- 2015: Rebuilding of methodology for MCEV calculation based on MVBS; weakness analysis and support in addressing weaknesses for Allianz SE, Munich
- 2014: Solvency II risk capital reporting project: validation of data submissions; analyses for management and the regulator; tool development; process and documentation improvements; training of team members for Aegon Group, The Hague, Netherlands
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
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
2.5 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
83%
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Zurich 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 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 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.
- 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, domain knowledge and machine learning to extract useful signals from structured and unstructured data. Specialists turn raw information into forecasts, classifications, recommendations, experiments and decision tools that support real business action.
What it builds
Data Science appears in products and internal systems across finance, healthcare, retail, manufacturing, mobility and technology. Typical deliverables include:
- Demand, risk and customer-churn models
- Recommendation and personalization systems
- Fraud, anomaly and quality-detection workflows
- Forecasting dashboards and decision-support tools
Ecosystem and tooling
Python and SQL are common foundations, supported by pandas, NumPy, scikit-learn, Jupyter and visualization tools such as Tableau or Power BI. Depending on the workload, specialists also use PyTorch or TensorFlow, Spark, dbt, cloud warehouses and orchestration tools for repeatable data pipelines.
When companies bring in specialists
Freelance expertise helps when a company has valuable data but lacks a clear path from exploration to production. It is useful for validating a new use case, improving an existing model, creating a forecasting process or connecting analytics with operational software. In Zurich, collaboration may combine remote delivery with on-site workshops and close coordination across multilingual teams.
What strong professionals deliver
Strong specialists define the business question before selecting a model. They test data quality, document assumptions, prevent leakage, choose meaningful evaluation methods and explain uncertainty to non-specialists. They also know when a simple statistical approach is more reliable than a complex machine-learning model.
Working with Data Science
The best engagement includes access to trustworthy data, a clear decision owner and a path to deployment and monitoring. Adjacent skills may include data engineering, software development, cloud infrastructure, product discovery and domain expertise. Professionals should leave behind reproducible notebooks or code, documented features, interpretable results and a plan for maintenance.
Frequently asked questions
What clients ask us most about Data Science — answered in short.
Companies use Data Science to forecast demand, detect fraud, segment customers, optimize operations and personalize digital products. The work can produce a model, an analytical study, a dashboard or a decision workflow connected to existing systems.
Data Science is a broad discipline that can include data analytics, statistical modeling, machine learning and experimentation. Data analytics often focuses on explaining past and current performance, while machine learning is a set of methods that Data Science specialists may use to make predictions or automate decisions.
A strong Data Science specialist usually works comfortably with SQL, Python, statistics and data visualization. Depending on the project, knowledge of data engineering, cloud warehouses, MLOps, product management or a specific industry can be just as important as modeling skill.
The right level depends on the data risk, business impact and production requirements, not simply on the size of the model. An exploratory analysis may need a focused specialist, while a regulated prediction system requires proven experience with validation, documentation, deployment and monitoring.
Much of Data Science can be delivered remotely when secure data access, documentation and regular communication are in place. On-site workshops can still help with problem framing and stakeholder alignment, especially when Zurich-based teams need close collaboration or specific language skills.
A useful Data Science brief states the business decision to improve, available data sources, security constraints, expected output and route to adoption. It should also identify who owns the decision and how success will be evaluated, rather than prescribing a model too early.
Review whether the specialist connects technical choices to the business objective and tests assumptions against realistic data. High-quality Data Science work is reproducible, transparent about uncertainty, robust against leakage and accompanied by clear documentation and a practical deployment or handover plan.
Data Science work commonly includes Python, SQL, pandas, NumPy, scikit-learn and Jupyter, with PyTorch or TensorFlow for deep learning. Specialists may also use Spark, dbt, cloud data warehouses, orchestration tools and visualization software when the work must run reliably at scale.
The average hourly rate of freelancers in Zurich, Switzerland who have used Data Science in their recent projects is 128 €, which corresponds to a daily rate of about 1,024 € 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, 83% 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 2.5 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 
Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
