Machine Learning Experts in Zurich
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Meet FRATCH Experts in Zurich, 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.
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.
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.
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
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
Lars Niedermeier
Last position:
Coordinator of a scientific OpenSource project
- Coordination of a scientific OpenSource project
- Administration of publication in the IEEE proceedings
- Presentation at the most prominent international conference for neural networks (WCCI IJCNN 2022)
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
25 years
Position duration
3 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Project Management
Top industries
Banking and Finance, Information Technology, Education
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
80%
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 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 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 it covers
Machine learning turns data into predictions, rankings, and decisions. It is used for fraud checks, demand forecasting, search relevance, and customer scoring. Strong experts know when to use classic ML, deep learning, or simpler rules.
Typical work
- Data preparation and feature engineering
- Model training, validation, and tuning
- Deployment for batch or real-time use
- Monitoring drift, latency, and quality
- Retraining pipelines and experiment tracking
Tools and stack
Common work spans Python, scikit-learn, TensorFlow, PyTorch, XGBoost, and notebooks. Experts also work with SQL, Spark, cloud services, and model tracking tools. The right stack depends on data volume, latency needs, and how the model will be maintained.
When to bring in help
Companies usually need freelance support when an internal team has data but no production-ready model path. That often means a pilot that must become stable, or an existing model that needs better accuracy, explainability, or monitoring. In Zurich, this is common in finance, insurance, health, and industrial software.
What strong specialists do
Good professionals do more than train a model. They define the target, check data leakage, choose proper metrics, and explain trade-offs clearly. They also write code that can be reviewed, tested, and maintained by other specialists.
Team fit
Machine learning projects often involve product, data, and software specialists working together. Remote work is common for model development and review, while on-site sessions help when data access, stakeholder input, or compliance questions need fast decisions. Clear communication matters as much as technical skill.
Frequently asked questions
Key details about Machine Learning, drawn from the questions we get asked most.
Machine Learning is used to predict outcomes, classify records, rank results, and automate decisions based on data. Common examples include fraud detection, churn prediction, recommendation systems, and demand forecasting. It is most useful when rules are hard to write by hand but past data contains patterns.
Machine Learning is the broader field. It includes methods like linear models, decision trees, random forests, and gradient boosting, while deep learning is a subset that uses neural networks. Many business problems are solved better with simpler models because they are faster to train and easier to explain.
A strong Machine Learning specialist needs a clear problem statement, access to relevant data, and a way to judge success. It also helps to know where the model will run, such as in a dashboard, API, or batch job. The better the brief, the faster the specialist can move from exploration to a usable result.
A strong Machine Learning profile usually includes Python, SQL, statistics, and solid data preparation skills. For production work, cloud services, container tools, version control, and basic software engineering practices matter too. Domain knowledge is also valuable because it helps separate useful signals from noise.
A simple proof of concept may only need one experienced Machine Learning specialist. A production system with monitoring, retraining, and integration into existing software usually needs broader experience with data quality, deployment, and testing. The main question is not the title, but whether the person has shipped similar work before.
Yes. Machine Learning work is often remote because data preparation, modeling, and review can be done with shared access and clear documentation. In Zurich, on-site meetings can still be useful for sensitive data, stakeholder workshops, or alignment with nearby business teams.
Look for a Machine Learning specialist who can explain why a model choice fits the problem, not just show a notebook. Good signs include careful validation, attention to leakage, clear metrics, and a plan for monitoring after release. Ask how they handled messy data, changing inputs, and failed experiments.
The main alternatives to Machine Learning are business rules, traditional statistics, and simpler automation. Those options can be better when the decision logic is fixed, the data is limited, or the result must be easy to audit. A good specialist will tell you when ML is not the right answer.
The average hourly rate of freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects is 124 €, which corresponds to a daily rate of about 993 € based on an 8-hour working day.
Of the freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects, 100% hold at least a Bachelor's degree, 80% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects have 25 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are German (100%), English (100%), and French (55%).
The most common industries among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Banking and Finance (82%), Information Technology (82%), and Education (64%).
The most common business areas among freelancers in Zurich, Switzerland who have used Machine Learning in their recent projects are Information Technology (91%), Product Development (73%), and Project Management (55%).
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.
Countries:
Zurich 
Geneva Basel Bern Request a free demo
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Philipp Thomaschewski
FRATCH CEO
