About me

Profile

Innovative and results-driven technology leader with 15+ years of experience driving AI solutions, scalable architectures, and data-driven decision-making. A strong blend of technical expertise, research background, and leadership skills, with a passion for transforming ideas into high-impact business applications.

Proven track record in AI-powered product development, R&D leadership, and software architecture, delivering solutions that enhance automation, analytics, and business intelligence. A strategic thinker who bridges the gap between technology, business needs, and innovation, ensuring long-term growth and competitive advantage.

Core Competencies

• Leadership & Strategy: Agile, Scrum, Kanban, Talent Retention, Team Scaling
• Technical Expertise: OOP, Clean Architecture (PHP, Python), Data Analytics (AWS Lambda, Tableau), AI & Machine Learning, Cloud Computing, SQL & NoSQL Databases (MySQL, MongoDB)
• Business Acumen: Budgeting, Roadmap Development, Product Strategy, Stakeholder Management
• Technical Tools: Git, Jira, MacOS/Linux, CI/CD, DevOps Practices

Experience

IAMIP Sverige AB – Chief Technology Officer (CTO)

February 2019 – Present

• Strategic Leadership: Defined and executed the company’s technical vision, aligning it with business goals. Contributed to product strategy and long-term roadmap.
• Team Management: Scaled and led a cross-functional team, mentoring staff and driving a culture of collaboration. Acted as Managing Director for the Polish entity.
• Product Development: Spearheaded architecture design, improved internal technical standards, and ensured product scalability and quality.

West Pomeranian Business School (ZPSB) - Lecturer

March 2025 – Present

• Courses: IT Project Management for IT students and AI and company digitalisation for EMBA students.

PayBreak Ltd (now Etika) – Data & Analytics Architect

January 2017 – January 2019

• System Architecture: Designed and implemented scalable architectures for financial systems, ensuring compliance with industry standards.
• Data & AI Initiatives: Developed AI-powered decision engines for automated risk analysis and financial reporting systems.

PayBreak Ltd (now Etika) – Senior Developer / Scrum Master

October 2013 – December 2016

• Development Leadership: Managed development flow, ensured product quality through CI tools, and introduced Test-Driven Development (TDD).
• Team Building: Trained and mentored junior developers, fostering a high-performance team.

Black on White sp. z o.o. – technical director

October 2011 – September 2013

• Led technical teams, delivered custom e-commerce solutions, and optimized SEO and eMarketing strategies.

Other

• Multideco.pl – IT manager (July 2010 – September 2011)
• Partydeco.pl – webmaster, PHP and MySQL developer (March 2010 – June 2010)
• Freelancer – webmaster/designer, PHP and MySQL developer (2004 - 2010)

Education

• Executive MBA: West Pomeranian Business School (2021 – 2023)
• MSc in Computer Science: West Pomeranian University of Technology (2005 – 2010)
• PhD Coursework: West Pomeranian University of Technology (2010 – 2013, incomplete dissertation)
• XIV High School in Szczecin (2002 - 2005)

Publications

An investment strategy based on the first derivative of the moving averages difference with parameters adapted by machine learning

Antoni Wilinski, Mateusz Sochanowski, Wojciech Nowicki Data Science in Finance and Economics, 2022, 2(2): 96-116. doi: 10.3934/DSFE.2022005
The article presents a certain investment strategy based on the difference between two moving averages, modified to allow the extraction of patterns. The strategy concept dropped the traditionally considered intersections of two averages and opening positions just after those intersections. Based on the observation of changes happening in the moving averages difference, it has been noticed that for some values of this difference and some values of additional strategy parameters, an interesting pattern appears that allows short-term prediction. These patterns also depended on the first derivative of the moving averages difference and the location of the current price relative to certain thresholds of the difference. Therefore, the strategy uses five parameters, including Stop Loss, adapted to the properties of the time series through machine learning. The importance of machine learning is highlighted by comparing simulation results with and without it. The strategy effectiveness was tested in the Matlab environment on the time series of the WIG20 (primary index of the Warsaw Stock Exchange) historical data. Satisfactory results were obtained considered in terms of minimizing investment risk measured by the Calmar indicator. on-line

Investment funds management strategy based on polynomial regression in machine learning

A. Wiliński, A. Smoliński, W. Nowicki Intelligent Systems for Computer Modelling: Proceedings of the 1st European-Middle Asian Conference on Computer Modelling 2015, EMACOM 2015
This paper presents the results of an investment strategy simulation. The strategy is based on common regression models in a time series, which yields the decision. A simple polynomial regression was the basic method used to achieve short-term value forecasts in the time series. Base params (number of steps in the past and a degree of a polynomial) were set based on a machine learning algorithm. The strategy is improved with some additional original (constitutes by the authors) parameters because using only the regression proved to be completely ineffective. Financial markets with bidirectional transactions (long and short transactions), as well as only long transaction markets, were both taken under research. on-line

Process of market strategy optimization using distributed computing systems

W. Nowicki, A. Bera, P. Błaszyński
Management and Production Engineering Review, volume 6, number 4, December 2015
If market repeatability is assumed, it is possible with some real probability to deduct short term market changes by making some calculations. The algorithm, based on logical and statistically reasonable scheme to make decisions about opening or closing position on a market, is called an automated strategy. Due to market volatility, all parameters are changing from time to time, so there is need to constantly optimize them. This article describes a team organization process when researching market strategies. Individual team members are merged into small groups, according to their responsibilities. The team members perform data processing tasks through a cascade organization, providing solutions to speed up work related to the use of remote computing resources. They also work out how to store results in a suitable way, according to the type of task, and facilitate the publication of a large amount of results. Read Article

Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning

A. Wiliński, A. Bera, W. Nowicki, P. Błaszyński
ISRN Artificial Intelligence, Vol. 2014
Paper examines two transactional strategies based on the classifier which opens positions using some rules and closes them using different rules. A rule set contains time-varying parameters that when matched allow to make an investment decision. Researches contain the study of variability of these parameters and the relationship between learning period and testing (using the learned parameters). The strategies are evaluated based on the time series of cumulative profit achieved in the test periods. The study was conducted on the most popular currency pair EURUSD (Euro - Dollar) sampled with interval of 1 hour. An important contribution to the theory of algotrading resulting from presented research is specification of the parameter space (quite large, consisting of 11 parameters) that achieves very good results using cross validation. Read Article

Meaning of simple rules in investment strategies of algorithmic trading

A. Wiliński, P. Błaszyński, A. Bera, W. Nowicki, K. Buda
Materiały IV Konferencji Technologia Edukacja Wiedza Innowacja, 24 IX 2013
In article are shown studies of the possibly simplest investment strategy of algo trading. The authors’ intention was to prove the thesis that in the greedy algorithmic trading directed to the proper response, not to forecast, there are almost always opportunities to achieve profit. This paper presents the results of tests for over twenty core markets - currency pairs, indices and commodities (eight results included). Introduced the original concept of quadrant of openings on each bar. In principle, the strategy is not relevant investment (studies were conducted without transaction costs), only cognitive in order to develop.

Organizacja zespołowego procesu optymalizacji parametrów strategii rynkowych

W. Nowicki, A. Bera
Modele inżynierii teleinformatyki, t. 8., p. 199-206, 2013 Read Article (in polish)

Wizualne gesty użytkownika w sterowaniu prezentacją

W. Nowicki, A. Nowosielski
Metody Informatyki Stosowanej, Nr 2/2011 (27), p. 97-103, 2011
In the article the problem of visual gesture recognition for presentation steering is presented. Based on camera image analysis the idea of steering is proposed. Presented system operates without a specialized controller and bases on visual gestures of the presenter. Conducted experiments with users demonstrated high usability of the system. Read Article (in polish)