Machine Learning Experts in Switzerland
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Meet FRATCH Experts in Switzerland, who have recently used Machine Learning
Gwang Jin Kim
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
Banashankari Naragundkar
Last position:
Senior IT Project Manager, Data and Integration Platform at MCH Group, Group IT
Led implementation of an Azure cloud-based, event-driven enterprise integration platform connecting Salesforce, Momentus(event) and ERP applications. Reduced overall costs by 60%, lowered operational errors to under 1%, and delivered on time.
- Led the full project lifecycle from initiation to delivery using agile and hybrid methods - platform vision, feasibility, technical blueprint, architecture, implementation, integration, testing, rollout and business adoption.
- Built the engineering team from the ground up, leading teams across Switzerland, Bulgaria and India using Scrum and Kanban; established Jira, Confluence, Asana and SharePoint for delivery and reporting.
- Established and managed frequent steering meetings, reporting that gave stakeholders transparency and drove key decision-making.
Mike Battistella
Last position:
Freelance UX-Researcher, -Designer at Companion Hub
UX research and UX concepts for an assisted living application with QT client dashboard/video application and web interface for client support
Niamh Manning
Last position:
Paid Services Consultant (Multi-Channel) at WPP Media
- Lead consultant for SEA campaigns with a senior focus on performance strategy, while managing multi-channel media campaigns across DV360, Meta, TikTok, and YouTube
- AI Marketing: WPP Media State of the Art AI technology
- Collaborate closely with planning and analytics teams to ensure cross-channel consistency, including audience overlap analysis across online video and CTV
- Manage accounts for large European / global brands such as MediaMarkt, Universal Pictures, Emmi, and Nestlé
Stefan Berreth
Last position:
Co-Founder at Stealth AI Infrastructure Deep-Tech Venture
- Deep-tech venture in enterprise AI model compression for on-premises and edge deployment.
- Built systematic R&D pipeline, business and technical architecture, early investor pipeline, and design partner network across AI verticals, datacentre operators, and robotics/edge computing — from zero.
- Ventures built concurrently, each in a fundamentally different regulatory and technical domain.
Ursula Maria Mayer
Last position:
Business Mentor at RoleModel Rebels
- Mentor female students and professionals in advancing their careers, particularly as aspiring tech entrepreneurs.
Patrick Riegler
Last position:
DevOps Engineer at Swisscard AECS AG
- PoC for various platform integration functionalities using Backstage and Crossplane
- Implementation of improvements of developer experience with Azure AVD and DevSpace
- Manage application deployment infrastructure on Kubernetes clusters
- Manage CI/CD platform
- Manage deployments into Kubernetes (Rancher and AKS) and into Azure
- Act as security champion of the DevOps team
- Manage vulnerabilities and support dev teams to manage third-party vulnerabilities using JFrog Xray
- Support building the platform team
- Manage infrastructure as code using Terraform and Atlantis
- Build and integrate a platform and IDP using Crossplane
Fabian Kostadinov
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
Matthias Isler
Last position:
Fractional CTO (Principal Engineer / Technical Architect)
- Designed large-scale systems and APIs serving thousands of concurrent users.
- Refactored a 650k-LOC monolith and led full AWS migration for stable performance.
- Introduced SLO-based observability, improving reliability and recovery flow.
- Optimised cloud and databases, achieving significant cost and latency reduction.
- Delivered LLM, RAG, and document-automation pipelines adopted in production.
Andreas Illig
Last position:
Business Development at SoftQuadrat GmbH
- Analyzed the current situation and repositioned the brand
- Built up recruiting, sales, and customer support
- Planned participation in trade shows
- Established a sales process
- Introduced a CRM tool (Pipedrive)
- Set up a subsidiary in Switzerland
- Implemented all aspects of staff leasing
Andrew Longe
Last position:
Service Manager - Testing at Takeda Pharmaceutical International AG
- Managing the Testing services for the Global IT Testing Centre of Excellence (TCoE) team, which is responsible for managing and supporting the Product & Project Testing Workstreams on various Regional Projects, especially in Pharmacovigilance, Digital Supply Chain, Clinical Practice and R&D, etc.
- Managing Client relationships, escalations and continuing to grow the project services globally, while also driving the testing efforts on Takeda’s AI, DevOps, Agile, and Digital projects.
- Led the implementation of a Quality Management System (QMS), ensuring compliance with GCP, GMP, and Swiss and EU regulations, resulting in a huge improvement in audit readiness.
- Overseeing projects for systems including ERP, EDGE, SAP ECC, SAP Transportation Management, S/4 HANA migration etc.
- Led an AI/ML PoC for AI implementation.
- Led a cross-functional team to ensure compliance with MDR, FDA regulatory guidelines and achieved successful project deliveries.
- Led and managed change management and communication for TCoE, keeping the Takeda organisation informed of change communication and practices, including presentations to various stakeholders.
- Ensuring TCoE outsource vendor resources adhere to regulatory, compliance and quality system standards and practices for GxP & non-GxP System Development Life Cycle (SDLC) including aSDLC and TCoE Global Standards.
- Translated the product vision, strategy and requirements into backlog items, which were prioritised based on potential business impact and customer value.
Ala Lutz
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
Hrvoje Kurtović
Last position:
Researcher, Investment Research Institute at Pictet Group
- Designed and implemented AI-enhanced macro-factor models improving asset allocation decisions across multi-asset portfolios
- Developed machine learning-based FX trading strategies, integrating macroeconomic and sentiment data to identify profit opportunities
- Models adopted by the investment committee to guide equity and currency exposure
- Collaborated with portfolio managers to translate analytical output into actionable investment insights
Karl Estermann
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
Rocco Ghielmini
Last position:
Senior Frontend Developer at Ginetta
- Contributed to the development of a platform for managing 3D data, similar to Google Earth, using React and Next.js.
- Designed and implemented intuitive and visually appealing user interface components.
- Technologies used: React, Next.js, WebGL, LuciadRIA
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
26 years
Position duration
2.6 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Project Management
Top industries
Banking and Finance, Information Technology, Professional Services
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
93%
Master's degree or higher
73%
Doctorate
20%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Switzerland 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 Switzerland using Machine Learning
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What ML covers
Machine learning turns data into predictions, classifications, recommendations, and automated decisions. It is used in fraud checks, demand forecasting, quality inspection, search, and personalization. Teams often shorten it to ML, and they usually compare it with rule-based systems or classic analytics.
Typical use cases
- Forecasting and anomaly detection
- Recommendation and ranking systems
- Document, image, and speech analysis
- Risk scoring and decision support
These projects appear in finance, manufacturing, retail, healthcare, and software products. In Switzerland, companies often need ML for regulated workflows, multilingual data, and systems that must integrate cleanly with existing business software.
Ecosystem and tools
Strong professionals work across Python, pandas, scikit-learn, PyTorch, TensorFlow, and xgboost. They also understand notebooks, feature engineering, experiment tracking, and model serving. Good ML work connects data preparation, training, validation, and deployment without breaking the product flow.
What strong experts deliver
A strong specialist does more than train a model. They define the target, choose suitable metrics, check data quality, and explain trade-offs clearly.
- Clean training and test data
- Reproducible model experiments
- Clear evaluation and error analysis
- Practical deployment guidance
When freelance help fits
Companies bring in freelance ML expertise when a project needs speed, a fresh view, or a narrow skill set. This is common for proof-of-concepts, model reviews, production fixes, and team support during delivery peaks. Remote work is often enough, while on-site collaboration helps when data access, stakeholders, or compliance reviews need closer coordination in Switzerland.
How to choose the right specialist
Look for experts who can explain why a model is suitable, how it will be validated, and what happens after launch. Ask for examples of shipped ML work, not just notebooks. The best specialists write readable code, communicate with product teams, and know when a simpler approach beats a more complex one.
Frequently asked questions
Quick answers to the questions that come up most around Machine Learning.
Machine Learning is used to turn data into predictions, rankings, classifications, and automated decisions. Companies use it for fraud detection, forecasting, recommendation systems, document analysis, and quality control. The best freelancers can show how the model supports a concrete business task, not just how it was trained.
Machine Learning is a part of AI that focuses on models that learn from data. Data science is broader and often includes analysis, reporting, and experimentation beyond model building. When hiring, look for someone who can connect the model work to your product, data pipeline, and operating constraints.
A Machine Learning freelancer is useful when you need specialist depth for a short period or your team is blocked on model quality, deployment, or data issues. Common cases include proof-of-concepts, model audits, production troubleshooting, and scaling an existing pipeline. This is also a good fit when the internal team has product knowledge but lacks ML depth.
A strong Machine Learning specialist usually brings Python, data preparation, feature engineering, model evaluation, and deployment awareness. Many projects also need experience with SQL, cloud services, APIs, and version control. If the use case involves text, images, or time series, relevant domain methods matter too.
Ask which problem the model should solve, how success will be measured, and what data is available. A good Machine Learning freelancer should also explain how they handle bias, missing data, overfitting, and maintenance after launch. If the answers stay vague, the project scope is probably still too broad.
Machine Learning work is often well suited to remote collaboration because data preparation, modeling, and review can happen asynchronously. In Switzerland, on-site time can help when access to systems, regulated data, or stakeholder workshops is important. The right setup depends on how much coordination your team needs around the data and decision process.
A solid Machine Learning deliverable has clear metrics, reproducible training, clean code, and a model that fits the business use case. It should also include error analysis, deployment notes, and a plan for monitoring after release. If the work stops at a notebook, it is usually not ready for production.
No, Machine Learning is not always the right answer. For some problems, rules, SQL logic, or standard statistics are easier to maintain and explain. A good freelancer will tell you when a simpler approach is enough and when a model will add real value.
The average hourly rate of freelancers in Switzerland who have used Machine Learning in their recent projects is 127 €, which corresponds to a daily rate of about 1,014 € based on an 8-hour working day.
Of the freelancers in Switzerland who have used Machine Learning in their recent projects, 93% hold at least a Bachelor's degree, 73% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Switzerland who have used Machine Learning in their recent projects have 26 years of professional experience, with a single engagement typically lasting around 2.6 years.
The most common languages among freelancers in Switzerland who have used Machine Learning in their recent projects are German (100%), English (100%), and French (59%).
The most common industries among freelancers in Switzerland who have used Machine Learning in their recent projects are Banking and Finance (82%), Information Technology (82%), and Professional Services (59%).
The most common business areas among freelancers in Switzerland who have used Machine Learning in their recent projects are Information Technology (88%), Product Development (71%), and Project Management (59%).
Main locations of FRATCH Experts, who have recently used Machine Learning
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.
Zurich Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
