Anne Charlotte Leysen, Developer in London, United Kingdom
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Anne Charlotte Leysen

Verified Expert  in Engineering

Data Scientist and Software Developer

London, United Kingdom
Toptal Member Since
February 3, 2020

Charlotte is an experienced professional in data science and analytics. She has worked in a range of industries, including finance, digital marketing, online marketplaces, and mental health. She has co-founded two startups, where her primary role focused on development, optimization, and business intelligence. Charlotte has a strong work ethic and strives for efficiency, delivering a high standard of work that brings top results.


Amazon Web Services (AWS), Tableau, HTML, JavaScript, SQL, Python
It's Ping
Amazon Web Services (AWS), Dialogflow, Node.js
Data Analytics, Machine Learning, Microsoft Excel, Qlik Sense, R, Python, SQL...




Preferred Environment

Tableau, Jupyter Notebook, Python, Amazon Web Services (AWS), Google Cloud, Azure, Google Data Studio, Databases, SQL

The most amazing...

...project I've developed was the code and system architecture for a startup I founded. I also advised on marketing, sales, and business development.

Work Experience


2017 - PRESENT
  • Founded an eCommerce B2B2C booking platform focused on the consumer experience industry through a mobile "empty chair marketplace." I was the head of data, analytics, technology, and finance.
  • Built the company analytics dashboard to include key operational and financial metrics data. Integrated data analytics knowledge as a strategic decision scientist with weekly growth metrics and user marketing.
  • Built a seamless cross-platform (web and iOS) application from scratch, created new features, and improved the UX and UI. There was a high focus on the complete automation of the platform.
  • Developed a strong understanding of the online retail market and business strategy in digital marketing, along with a strong proficiency in SEO, Swift, and LAMP.
Technologies: Amazon Web Services (AWS), Tableau, HTML, JavaScript, SQL, Python


2019 - 2020
It's Ping
  • Developed a web-based chatbot using Dialogflow (a Google NLP tool) and Node.js. Created an online dashboard to view data collected from the chatbot with real-time statistics and aggregated metrics.
  • Collaborated with the co-founder to create a seamless, empathetic, engaging chatbot for employees at work. The chatbot contains several modules to help identify specific work or home problems and overall moods.
  • Oversaw the chatbot's training with testers and round-table sessions. This helped improve our product and pivot to a better version.
Technologies: Amazon Web Services (AWS), Dialogflow, Node.js

Senior Data Analyst

2019 - 2020
  • Developed testing standard practices (A/B tests and pre-post testing) and conducted several experiments to optimize Expedia's global search engine marketing.
  • Created automated scripts using python to generate keywords and ads for Expedia's digital marketing strategy.
  • Developed trading and monitoring dashboards for operations teams to use daily and track performance. Predominately used BigQuery to pull the data into a Tableau dashboard, which refreshed automatically.
  • Conducted data analysis to aid strategic decision-making within strategy and marketing teams. Worked on Python and R and explored data to provide valuable insights and recommendations to use in this eCommerce-based marketing business.
  • Developed the eCommerce advertising platform in Google Ads and helped to optimize the framework for the automation and implementation of thousands of campaigns and keywords.
  • Developed several ML models to predict the revenue of new hotels onboarded into Expedia. The model outcomes were productionized and used to drive marketing efforts and spending.
Technologies: Data Analytics, Machine Learning, Microsoft Excel, Qlik Sense, R, Python, SQL, Google BigQuery, Data Analysis, Pandas

Research and Sales Analyst

2014 - 2016
Deutsche Bank
  • Used statistical analyses to predict market trends and pitched macro trade ideas to investors, published market reports, optimized prediction trackers, created carry trade models, and produced market updates for the CEO.
  • Executed FX transactions for asset management clients in spot, forward, and derivative products. Involved in the execution of exchange transactions of over £100 million in size.
  • Coordinated a TEDx event featuring speakers on health and business. Responsible for pitching the initiative to senior management. Managed the entire process within the bank and hosted the speakers at the event.
  • Accredited with the sole responsibility of chairing the "Ideas Lab" speaker series venture. Managed and led all aspects of the program, including hosting events, sourcing speakers, and internal marketing.
Technologies: Python, Microsoft Excel, Bloomberg, Data Analysis

Data Engineering and Mathematics: Simulation of Large Crowds | Crowd/Crisis Management

As part of a mathematical consultancy team, I created a model to simulate large crowds in public areas during a crisis situation, e.g., an attack or crushing incident.

Detailed OS maps were ingested (from the UK) to create a base landscape of buildings, streets, and pavements. A crowd was spawned onto the map with varying demographics (age, fitness, gender), and various stress responses were simulated for each crowd member, e.g., flight, fight, freeze, hide, etc.

The entire codebase was created with Python and hosted in AWS cloud. A UI/front end was built for users to create new simulations and view simulation results.

We did in-depth, real-world research on crowd behaviors and psychology for each new feature. I was responsible for translating the research into a form that code be coded, and I helped code many of the features implemented in the model.

Data Science: Home Hazard Predictive Machine Learning Model

As part of a team, we created a home hazard model that would predict category-1 hazards in homes across the UK. The hazards we focused on included dampness and mold, excess cold, fire, falls, and electrical.

The training dataset included address-level data on the buildings' construction, e.g., wall material, insulation materials, types of boiler, etc. This was enriched with other data, such as weather and poverty metrics.

We used supervised learning models such as logistic regression, SVM, and a random forest. We also experimented with clustering algorithms and Naive Bayes classifiers. The final outputs included low-, medium-, and high-level risk predictions for thousands of households across the UK.

Data Science | Data Engineer: Financial Research Company

Created a data platform and automated pipeline from scratch for a financial research company based in London, UK. I hosted a daily data pipeline feed on Azure Cloud using Data Factory and Databricks.

The data pipeline consists of fetching data from various sources via APIs, ingesting it as raw files, processing it through a validation pipeline, and cleansing it into a final processed dataset. I built several dashboards on Power BI based on the data to aid the business in decision-making.

Created time series data pipeline from web scraping sites using Python, such as flight tracking data.

Data Science: AWS Support Function for Joint BioSecurity Centre

Supported over 200 users in data science and data analytics functions on the AWS SageMaker machine learning platform. I resolved complex problems relating to predictive analytics and visualizations for the Joint BioSecurity Centre in the UK, focusing on COVID-19-related data and situational awareness.

Data Engineering: A/B Testing and Experimentation Platform for YouTube Content Creators

Collaborated in a cross-functional team with engineers and a product manager to define content performance metrics and a supporting data model in Amazon Redshift and produce analysis and self-service dashboards on top of this data, including experiment analysis and tracking dashboards.

My responsibilities included refining and validating the supporting data model in Amazon Redshift. I used SQL to explore data and identify necessary changes, collaborating with engineers to implement changes.

I also defined performance metrics based on exploratory data analysis, product considerations, and statistical power analysis, such as tweaking metric definitions to allow A/B tests to run faster. I built self-service experiment analysis and metric tracking dashboards using SQL and Python in a Sisense visualization product.

Data Analysis: Digital Marketing for Expedia

Focused on data analytics and data science in search engine marketing and SEO for at Expedia.

I challenged digital marketing strategies and improved growth by running incrementality tests such as pre- and post-testing, A/B testing, and time series modeling. I also found opportunities for collaboration between the Expedia brands to leverage our combined market share. I used various modeling techniques in R and Python, data preparation in SQL, and data visualization using Tableau and Qlik Sense.

Entrepreneurship: EmptyChair Technical Co-founder

An online marketplace, eCommerce website, and iOS app focused on selling creative workshops and experiences.

I co-founded and built the website and back end to run the platform entirely and automatically. The platform grew to over 10,000 users in the 1st year and registered over 200 partners.

Data Analysis: Churn Analysis for Company Optimizing YouTube Content Creators

Conducted a three-week analysis on churn rates of a monthly subscription-based product. I brought together multiple data sources spread across 3rd-party data integration platforms and internal data warehouses, including ChartMogul, Amplitude, and Amazon Redshift.

I also explored key drivers of user cancellations, including user demographics, properties, and behaviors. I built a pipeline to assess feature importance using an XGBoost model. I produced graphical charts to analyze patterns and correlations across cancellation rates and features. Finally, I leveraged ChartMogul's churn rate calculations regarding the customer, net MRR, and gross MRR to build a coherent story around the development of the churn rate over time.

Data Analysis: Marketing Attribution Models

Developed tools to calculate marketing attribution across channels, such as search engine marketing, brand, programmatic ads, and direct. I incorporated seasonality as a separate model to improve the estimation. The tools were used to allocate marketing capital each quarter.

Data Science: Hospital Management

To improve hospital capacity management, our team developed a predictive model to identify how long a new patient at a hospital would likely need to spend in a hospital bed. This would be based on the patients' initial screening metrics and demographics, such as blood pressure and cholesterol levels. This project was part of a "Health Hackathon" where doctors came together with data scientists and jointly developed a solution.


Microsoft Excel, Tableau, Microsoft Power BI, Amazon SageMaker, BigQuery, Bloomberg, Dialogflow, Qlik Sense, Sisense, AWS CLI


Jupyter Notebook, Amazon Web Services (AWS), Azure, Databricks, RStudio


Visualization, Data Analysis, Data Analytics, Data Visualization, Dashboards, Google BigQuery, Machine Learning, Statistics, Predictive Modeling, Artificial Intelligence (AI), Web Scraping, Data Scraping, Neural Networks, Azure Data Factory, Data Engineering, A/B Testing, Predictive Analytics, Amplitude, eCommerce, Google Data Studio, Economics, Econometrics, Mathematics, Entrepreneurship, Web Development, Business, Marketplaces


Python, R, SQL, HTML, JavaScript


NumPy, Pandas, Node.js, TensorFlow


Data Science


MySQL, Data Pipelines, Redshift, Google Cloud, Databases

2017 - 2018

Master's Degree in Business Analytics and Big Data

IE Business School - Madrid, Spain

2011 - 2014

Bachelor of Science Degree in Statistics and Economics

University College London - London, UK