Lucas Reymond

Master of Science (MSc) in Statistics from EPFL - École Polytechnique Fédérale de Lausanne

Hi, I'm Lucas.
I'm a statistician, a data enthusiast, looking for a job.

Statistics Machine Learning Causal inference Data science Time series analysis Pattern recognition Optimization NLP

Regression modeling Reinforcement learning Probability LLM Bayesian statistics Generative AI Stochastic processes Neural networks

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About me

With a Master of Science (MSc) in Statistics from EPFL, I am passionate about applying statistical modeling, quantitative methods, and data science to solve real-world challenges. With a track record of academic excellence, including ranking in the top 2% of my class during my Bachelor’s degree and achieving a 5.5/6 GPA in my Master’s program, I excel in my field.

My journey has shaped me into someone who stands out for my attention to detail, strong analytical skills, and innovative mindset. I love working with others and enjoy collaborative environments, especially multidisciplinary teams where different perspectives bring innovation. As a strong team player with excellent communication skills, I actively contribute to discussions, translate complex ideas into a clear vision, and contribute to a dynamic team culture. It is clear to me that teamwork increases creativity and leads to more impactful results, whether in research, data analysis, or problem-solving.

My strong academic and practical background covers both Statistics and Data Science fields. My key strength lies in my ability to apply rigorous statistical theory and mathematical models that I can fully understand, alongside modern data science techniques, to solve real-world problems and answer complex questions. Here are the core skills I developed in both statistics and data science and which I can rely on:

•Regression modeling

•Causal inference

•Bayesian statistics

•Time series modeling

•Probability theory

•Hypothesis testing

•Statistical simulation

•Stochastic processes

•Multivariate processes

•Optimization theory

•Model selection, optimization and validation

•Descriptive statistics

•Supervised and unsupervised learning algorithms

•Bayesian machine learning

•Reinforcement learning

•Neural networks

•Natural language processing

•Large language models

•Generative AI

•Pattern recognition

•Algorithm development

•Data cleaning and preprocessing

•Data visualization

•Data interpretation

Education

I have recently completed and earned my Master’s degree (MSc) in Statistics from EPFL, in October 2024. This achievement marks the beginning of a new chapter, and I am looking forward to applying what I have learned to real-world challenges and continuing growing.

EPFL - Ecole Polytechnique Fédérale de Lausanne

Master of Science (MSc) in Statistics. 5.5/6 GPA, 2022 – Oct. 2024.

Aalto University

Third year of Bachelor of Science (BSc) in academic exchange in Helsinki – Finland, 2021-2022.

EPFL - Ecole Polytechnique Fédérale de Lausanne

Bachelor of Science (BSc) in Mathematics, ranked in the top 2%, 2019 – 2022.

Experiences

I am impatient to gain more real-world experience where I can apply and develop my skills. I am highly motivated and excited to learn through new challenges.

Research Intern - EPFL

Six-month research project on the Validation of the Extremal Behaviour of Rainfall Generators, in

I conducted research on the Validation of the Extremal Behaviour of Rainfall Generators, using Extreme Value Theory. I applied statistical models, including Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD), to analyze rainfall data. I had the opportunity to work as part of a multidisciplinary team of researchers to ensure accuracy in climate and hydrometeorological simulations. At the end, I created a research poster to present my findings to the Mathematics section at EPFL, effectively communicating key insights.

During this six-month research project, I gained expertise in data analysis, statistical modeling and interdisciplinary collaboration for research.

Lausanne, Mar. 2024 – Aug. 2024.

R&D Master Trainee in Statistics - Nestlé Research Center

During my six-month internship at the Nestlé Research Center as an R&D Master Trainee in Statistics, I focused on applying advanced statistical modeling and programming techniques to complex data from clinical trials. A significant part of my work involved developing efficient algorithms and R code to compute the Fragility Index, a metric used to assess the robustness of clinical trial outcomes. I also contributed to exploratory data analysis and created statistical models to analyze and interpret diverse datasets.

In addition to working on clinical data, I applied my skills in data visualization using R and various libraries like ggplot2 to create insightful graphics for decision-making. This internship gave me hands-on experience with statistical programming, hypothesis testing, regression analysis, and large-scale data processing, allowing me to enhance my expertise in quantitative methods and advanced statistical techniques.

This experience strengthened my ability to design and implement data-driven models, improve workflow efficiency, and contribute to innovative research within a dynamic, interdisciplinary team.

Lausanne, Sep.2023 – Feb. 2024.

Private Tutor

I helped students to excel in mathematics across various levels, from high school to university. I provided personalized support in probability, statistics, algebra, analysis, optimization, and machine learning. Over the years, I’ve worked with 40 students, aged 12 to 40, tailoring my approach to fit their unique learning needs.

This experience has taught me how to effectively convey complex mathematical concepts to individuals with varying levels of mathematical background, making advanced topics accessible to those who may not have a deep understanding of the subject.

Sep. 2020 – Oct. 2024.

Internship - Jane Street

Selected to join a competitive program (Quantitative Trading Camp) exploring real-world applications of mathematics and probability in quantitative trading. I engaged in interactive group activities, mock trading sessions, and lectures on topics such as probability, market structure, and arbitrage.

I gained insight into the diverse roles within Jane Street and enhanced my understanding of financial markets and quantitative strategies.

London, Oct. 2023.

Class Representative

Lausanne, Sep. 2023 – Sep. 2024.

Team captain in competitive trivia quiz tournaments

As the captain of an EPFL’s student trivia quiz team, I lead a group of teammates in competitions that challenge our knowledge, teamwork, and quick thinking.

This experience sharpens my analytical skills, reinforces my ability to perform under pressure, and strengthens my leadership in a dynamic, fast-paced environment. Plus, it’s a great way to learn, have fun, and push intellectual boundaries.

Lausanne, Sep. 2023 – Sep. 2024.

Head of Events Team - Data Analytics Group

As Head of the Events Team at the Data Analytics Group, a student association at EPFL, I lead the planning and organization of various data science events, including workshops, guest lectures, and coding challenges. In this role, I oversee a team, ensuring successful event execution and engaging the EPFL community in data-driven activities.

This leadership experience has strengthened my skills in team management, event coordination, and fostering connections within the data science ecosystem.

Lausanne, Sep. 2022 – Aug. 2023.

Teaching Assistant - EPFL

As a Teaching Assistant at EPFL, I supported students in understanding and mastering complex concepts across various mathematics courses. My role involved helping students from different promotions navigate topics, assisting them with lectures, and guiding them through their exercises.

Strengthened my ability to support others by providing personalized guidance, fostering a collaborative learning environment, and adapting explanations to meet individual student needs.

Lausanne, Sep. 2020 – Jul. 2023.

Research Intern - Aalto University

Six-month research project, working on the statistical analysis and modeling of cryptocurrency data.

Helsinki (Finland), Jan. 2022 – June 2022.