Marcin Wylot, PhD

Data Scientist & Machine Learning Engineer

He has almost 20 years of experience in Computer Science both in industry and academia. He is highly oriented on providing solutions to increase incomes and to optimize costs with a simplest possible set of means for a problem (KISS paradigm), often with non-standard but efficient and effective approaches.

He has worked with a high variety of platforms and solutions involved in the whole data processing pipeline. From gathering, modeling, storing, data, trough analyzing, finding insights in the data, up to building and deploying in production machine learning models.

He also has experience in presenting results in public gained at top scientific conferences, as well as, giving academic lectures and mentoring younger colleagues. As an independent academic researcher, he had a chance to developed great time management and organizational skills.

Moreover, he had an opportunity to coordinate work of data processing teams in different startups, to supervise students towards their master and PhD diplomas, and to coordinate efforts of experienced scientists working on research projects.


  • Artificial Intelligence
  • Big Data
  • Semantic Web & Linked Data
  • Business Process Modeling


  • PhD in Computer Science, 2015

    University of Fribourg -- Switzerland

  • MSc in Computer Science, 2010

    University of Lodz - Poland

  • MSc in Computer Science, 2009

    Université Claude Bernard (Lyon I) - France

  • BSc in Computer Science, 2006

    WIT under the auspices of Polish Academy of Sciences

Data Science

Machine Learning

Big Data





Ischemic Stroke Risk Assessment

The goal of this project was to develop an AI backend engine for an intelligent decision support system which asses an ischemic stroke …

Live Contract Trading Data Harvesting

The goal of this project was to build an ETL system collecting and computing data about trades and trading positions for various …

Expert System for Blood Management

The goal of this project was to develop a rules-based decision support system. The solution was assisting medical doctors to make fast …

Predicting Blood Transfusion Needs

The goal of this project was to develop an AI-backed intelligent decision support system assisting medical doctors to make decision …

SolarData - Simulation the Performance of Photovoltaic Energy Systems

This platform allows to simulate the amount of energy produced by a photostatic energy system. It extracts weather data from grib files …

Machine Learnin backed FAQ Chatbot

The goal of this project was to develop an AI backend for a FAQ chatbot. A client had collected significant amount of questions and …

ETL system for cryptoassets transactions data

The goal of this project was to build an ETL system collecting and aggregating data about various cryptoassets, from multiple data …

Anomaly Detection in Business Intelligence Smart Metrics

The goal of this project was to develop an anomaly detection pipeline for business intelligence systems in order to trigger a BI report …

TripleProv - RDF Provenance

TripleProv is an in-memory RDF database capable to store, trace, and query provenance information in processing RDF queries. TripleProv …

DiploCloud - Scalable Distributed RDF Data Management System

dipLODocusRDF is a system for RDF data processing supporting both simple transactional queries and complex analytics efficiently. …


Data Science and Machine Learning Consulting

The key aspect of data science is to closely work with business stakeholders to understand their goals and determine how data can be used to achieve those goals. As an output in this process we want to makes value out of data. Data Scientist gather information from various sources and analyzes it in order to understand how your business performs. Working with large data sets and using them to identify trends we reach meaningful conclusions to inform strategic business decisions.

Data Engineering and Big Data Consulting

Data Engineer plays a key role in building a data-driven solutions by maintaining data pipelines and databases for ingesting, transforming, and storing data. Data Engineer supports data science by connecting data from one source to another and transforming data from one format to another. Data Engineer builds data infrastructures connecting data ecosystems to make a data scientist's work possible. The core hard-skills in day-to-day work: Data platforms - relational databases, NoSQL, Hadoop, Spark, Kafka, RabbitMQ , Flink, etc… ETL - tools to extract data, move data around and transform them for ingestion into data platforms (eg.

DevOps Consulting

DevOps Engineer works with developers to facilitate better coordination among operations, development and testing functions by automating the integration and deployment processes.

DevOps helps in:

  • Increasing business agility
  • Reducing time to market cycle
  • Increasing quality and confidence in code
  • Reducing cost of quality
  • Increasing productivity
  • Reducing cost
  • Increasing system uptime

DevOps consulting covers the following areas:

  • CI/CD pipelines (Git, Jenkins, Puppet, Chef, GitHub, GitLab)
  • Automatic deployment
  • Configuration and optimization of Linux environments (Linux, AWS, GPC, Azure, Cloud, on-premise)
  • Deploy virtualization & containerization (VMware, VirtualBox, Docker, docker-compose, Swarn, Kubernetes, Podman)
  • Monitoring performance and outages (TICK, Prometeus, Nagios)
  • Test automation withing the CI/CD pipelines
  • Consulting on selecting solutions and design decisions with scale, performance, and operability in mind
  • Implementing integrations
  • Deploing updates and fixes
  • Root cause analysis for errors
  • Investigating and resolving technical issues
  • Design procedures for system troubleshooting and maintance
  • Migrating of infastractures between cloud prividers, on-premise to cloud, and cloud to on premise, hybrid cloud&on-premise
  • Database configuration and tuning (MySQL, PostgreSQL, MongoDB, Redis, Riak, Casandra, InfluxDB, Neo4j, ask for more)

Software Consulting

Software Consultant advises clients on how to architect and develop large applications. Helping also to technically shape a dev team, the maintain software development and infrastructure ecosystem. Software consulting covers the following areas: Developing application concept Advising on technology selection Crafting a technical architecture Delivering a detailed integration roadmap Optimizing operational costs of a system Ensuring scalability of the system to handle the load as your business grows. Ensuring the system to be maintainable and extensible so that developers can easily make the changes and add new features, when your business needs them.