MSc in Financial Technology and Data Science
Supported by ensIIE, a prestigious engineering school specializing in applied mathematics and computer science, this Master's degree offers a unique interdisciplinary approach combining quantitative finance, artificial intelligence, blockchain and advanced computing.
Objectives of the programme
The programme prepares graduates to bridge traditional financial modelling and modern digital technologies. By completing this programme, students will build expertise in
Mathematical and statistical foundations (probability, stochastic processes)
Data science principles and advanced modelling techniques
Integration of modern technologies (AI/Machine Learning, Blockchain, Quantum Computing).
Algorithmic development and scientific computing
Risk management and financial regulation (RegTech)
Audience concerned and Prerequisites
Profiles sought
This training is mainly aimed at STEM graduates (Science, Technology, Engineering, Mathematics) wishing to move towards FinTech.
Academic prerequisites
Diploma: Hold at least a Bachelor's degree (License or Bac +3) in Mathematics, Statistics, Computer Science, Economics or Quantitative Finance.
Technical skills: Solid foundation in mathematics (probability/statistics) and programming (Python, R, C or Java).
Language level: A minimum B2 level in English is required (certified by IELTS 6.5, TOEFL iBT 90, TOEIC 850 or equivalent).
Note: An exemption may be granted to up to 20% of candidates with significant professional experience (more than two years) or demonstrated exceptional motivation.
Training program
The training lasts 4 semesters, including 95 ECTS of courses, an internship (15 ECTS) and a Master thesis (15 ECTS).
Structure of lessons
Semester 1 (Core) : Stochastic processes, Data analysis, Advanced programming (C++), Operations research, Functional analysis.
Semester 2 : Statistical modeling, Big Data, Machine Learning, Financial instruments and start of options.
Semester 3 : Financial risks, Trustworthy AI, Green IT, and in-depth specialization.
Semester 4 : An elective course (Blockchain, Crypto-markets or Low Latency) followed by the internship and the thesis.
Specialization Pathways
from semester 2, students may choose elective courses from one of three pathways or combine electives across pathways :
PMF : Probability and Mathematical Finance.
SDS : Statistics and Data Science.
ORQ : Operations Research and Quantum Computing.
Internship and Master Thesis
The last semester (from April) is dedicated to a compulsory professional internship of at least 4 months in a company or research center. At the same time, the student prepares a Master's thesis under the supervision of an academic tutor, with a defense planned for September.
Professional opportunities
Graduates occupy hybrid positions with high added value :
FinTech Engineer / Blockchain Developer.
Data Scientist specialized in finance.
Quant (Quantitative Analyst).
Specialist in AI and financial Machine Learning.
Technology risk manager.
Calendar and Registration
Admission is on file via an online portal, followed by an interview with the jury.
Session | Application Deadline | Interviews | Final Results
Round 1 | March 30 | April 20-25 | April 30
Round 2 | April 30 | May 20-25 | May 30
Round 3 | May 30 | June 20-25 | June 30
Round 4 | June 30 | July 20-25 | July 30
Opening of applications : January 2026.
Return to school : September.
Tuition fees
Initial training (non-EU) : €4,750.00/year
Initial training (EU) : €6,750/year
Continuing education ( excluding VAT) : €6,000.00/year
Diploma obtained
At the end of the training, students who have validated all the modules and successfully defended their thesis obtain the title of Master of Science (MSc) Financial Technology and Data Science, a diploma accredited by the Conference of Grandes Écoles (CGE), corresponding to 125 ECTS credits.