Research, in Motion.

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Research on interpretable machine learning, AI exposure, and financial markets.

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Curriculum vitae

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Curriculum vitae · Yanick Annema

Yanick Annema

Financial Data Engineer · Moni IT & ConsultingEnschede, NL · Dutch (native) · English (fluent)

Quantitative finance and machine learning, applied to questions about firms rather than to models for their own sake. The work is empirical: assemble the data, fit an interpretable estimator, then state what it does and does not support.

01

Education

MSc Financial Engineering & Management

University of Twente · graduated July 2026

  • Coursework: Structured Financial Products 9.0, Applied Statistical Learning 8.8, Machine Learning 8.7, Risk Management, and Applied Corporate Finance.
  • Projects: Deep Q-Network trading-agent benchmarking under realistic market frictions; random-forest modelling of corporate capital structure against linear baselines.

Exchange programme, Finance

University of Melbourne

Advanced coursework in applied corporate finance and strategy, M&A, IPOs, LBOs, hedging, and valuation.

BSc Applied Physics

Saxion

Quantitative and analytical foundation in programming, data analysis, and computational modelling. Propaedeutic-year GPA 8.8.

02

Experience

Financial Data Engineer

Moni IT & Consulting B.V. · part of Moore MKW

Builds internal corporate-finance tools, data pipelines, and measurement systems; contributes to AI implementation across professional-services workflows.

Corporate finance analyst

Moore MKW Corporate Finance

Builds data and automation tools supporting deal evaluation and financial analysis, alongside valuation and transaction execution.

  • Built Python pipelines and internal applications that structure permitted financial and transaction data for analysis.
  • Developed document-processing workflows that transform approved source files into structured analytical inputs.
  • Built interpretable machine-learning research prototypes using XGBoost and SHAP for questions about post-merger operating performance.
  • Supported DCF/APV valuations, information memoranda, and teasers.

Research internship

Saxion

Optimised chip placement by simulation and delivered validated adhesive application parameters.

Engineering internship

Thales Group

Designed and tested a radar cooling system and improved experimental accuracy with a robust testing framework.

Karate instructor

Muga Mushin Ryu

Instruction across age groups; eight students coached to black belt.

03

Instruments

Programming & ML

Python · XGBoost · SHAP · scikit-learn · pandas · NumPy · R · MATLAB · SQL/PostgreSQL · VBA/Excel

Finance & analysis

M&A · DCF/APV valuation · LBOs · IPOs · risk management · structured products

Research data

LSEG · Worldscope · public filings · transaction and financial databases

04

Research interests

Empirical AI economics and labour-market dynamics: adoption gaps between firms, task displacement in professional services, and the effect of AI on skill formation. The method remains empirical: measure observable workflow change and keep inference within the evidence.

Yanick Annema · curriculum vitaeReferences available on request
PortraitHalftone · 7 px pitch

Subject · Yanick Annema

Method / Instrument

The path finder

Fig. 01 — working methodDrag to rotate, or focus the scene and use the arrow keys

Stage 01

Question

Start with something that can be tested.

Make the reasoning inspectable.

Fig. 01 · schematic · no fitted values

Approach

Build the system the question needs.

I work where finance, empirical research, and software meet. I begin with the question and the available evidence, then build the data and technical system needed to test it.

Models are useful when their assumptions, uncertainty, and limits remain visible. The same principle shapes the tools and explanations I build: rigorous enough to inspect, clear enough to reuse.

Contact

Contact

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