Christopher is available for hire
Hire ChristopherChristopher Hemmens
Verified Expert in Engineering
Data Science Developer
Location
Lausanne, Switzerland
Toptal Member Since
August 27, 2021
Christopher is a statistician and data scientist with a PhD in behavioral economics. He specializes in mathematical modeling, forecasting, and building tools in Python. He has helped businesses in hospitality, smart home controls, and retirement planning, among others, boost the quality and range of services they offer their clients.
Portfolio
DVX HVAC CO
Data Science, Python, Time Series, Applied Mathematics, Python 3...
Second 50 Financial, LLC
Mathematics, Mathematical Finance, Data Science, Regression Modeling...
Jeremy Beasley
Causal Inference, Python, Regression Modeling, Quantitative Analysis...
Experience
Availability
Part-time
Preferred Environment
Python, Pandas, SQL, Data Engineering, Statistical Modeling, Machine Learning, Data Science, Mathematical Modeling, Analytics, Financial Modeling
The most amazing...
...app I've built forecasts retirement savings sustainability decades into the future based on a given strategy of stocks and bonds, which runs in minutes.
Work Experience
Data Scientist
2024 - 2024
DVX HVAC CO
- Used mathematical modeling to derive the theoretical upper or lower limit for temperature within a space during periods when the heater or cooler was on, generating a target variable for ML models that physically doesn't exist.
- Devised a way to visualize temperature degradation rates to identify location idiosyncracies in terms of rates of temperature change, allowing engineers to tailor solutions to specific locations.
- Devised a way to estimate power consumption for a space, based solely on the square footage, providing the client's customers with a way to estimate future energy costs.
Technologies: Data Science, Python, Time Series, Applied Mathematics, Python 3, Statistical Methods, Machine Learning, Data Modeling, Jupyter Notebook, Data Scientist, Statistical Analysis, Predictive Modeling, Mathematics
Developer
2023 - 2023
Second 50 Financial, LLC
- Built a statistical model of the US stocks and bonds markets and used it to generate Monte Carlo simulations for statistically robust retirement savings forecasting.
- Used Markov chain analysis to model the US federal interest rate, providing a realistic baseline above which to forecast individual retirement savings sustainability.
- Built a minimum viable product for forecasting using loops and then solved the problem analytically, decreasing runtime from close to an hour to seconds.
Technologies: Mathematics, Mathematical Finance, Data Science, Regression Modeling, Quantitative Analysis, Backtesting Trading Strategies, Regression, Data Reporting, Minimum Viable Product (MVP), Quantitative Research, Quantitative Modeling, Financial Analysis, Reporting, Quantitative Finance, Software Development, Python 3, Statistical Methods, Time Series, Machine Learning, Data Modeling, Jupyter Notebook, Data Scientist, Statistical Analysis, Predictive Modeling
Causal Inference Expert
2023 - 2023
Jeremy Beasley
- Used panel regression techniques to determine the effect that advertising spend has on revenue, allowing the client to optimize advertising spend.
- Used a difference-in-difference analysis to determine the effect that advertising spend has on revenue, allowing the client to optimize advertising spend.
- Completed full analytical reports on the effects of marketing spend with full data visualization in Jupyter Notebook, allowing the client to easily see key takeaways and business insights.
Technologies: Causal Inference, Python, Regression Modeling, Quantitative Analysis, Regression, Data Reporting, Marketing, Reporting, Python 3, Statistical Methods, Time Series, Data Science, Data Modeling, Jupyter Notebook, Data Scientist, Statistical Analysis, Mathematics
Scientific Collaborator
2023 - 2023
HEIG-VD
- Wrote a paper on a novel technique to construct Model-X knockoffs using linear algebra, a framework designed to mitigate the false discovery rate (FDR) in machine learning models.
- Wrote optimization code in various languages, including MATLAB, TensorFlow, and SciPy, and determined the best method for achieving satisfactory code speed and value convergence targets.
- Used variational autoencoders to design index-replicating stock portfolios, thereby mimicking the index fund while minimizing transaction costs.
Technologies: Python 3, SciPy, MATLAB, Optimization, Linear Optimization, Financial Data, Variational Autoencoders, Machine Learning, Deep Learning, Neural Networks, APIs, SpaCy, Models, Modeling, EDA, Exploratory Data Analysis, Quantitative Analysis, Backtesting Trading Strategies, Regression, Data Reporting, Writing & Editing, Content Writing, Trading, Algorithmic Trading, Minimum Viable Product (MVP), Quantitative Research, Quantitative Modeling, Financial Analysis, Reporting, Software Development, Statistical Methods, Data Science, Data Modeling, Jupyter Notebook, Data Scientist, Statistical Analysis, Predictive Modeling, Mathematics
Data Science Engineer
2022 - 2022
Comniscient Technologies LLC dba Comlinkdata
- Compiled dozens of reports detailing important differences in two telecoms datasets (big data). Every report included visualizations built in PyPlot and Seaborn designed to highlight key information.
- Conducted complex data analysis on telecoms data from Japan and Canada as a result of unusual activity. Identified key issues and suggested potential causes and fixes.
- Designed reusable dual SQL queries, each one applied to two datasets to be directly compared, one on Google BigQuery and one on Amazon Athena.
- Used geospatial data extensively as a method for aggregating telecoms data (big data).
Technologies: Data Science, Statistics, Data Analysis, Python, R, Networking, Amazon Web Services (AWS), SQL, Analytics, Google BigQuery, BigQuery, Data Queries, PostgreSQL, Data Pipelines, Big Data, MySQL, Modeling, EDA, Exploratory Data Analysis, Quantitative Analysis, Data Reporting, Reporting, Python 3, Data Modeling, Jupyter Notebook, Amazon Athena, Data Scientist, Mathematics
Data Scientist
2021 - 2022
Toptal Client
- Built a data cleaning pipeline to produce high-quality data from multiple databases of generic multinational business data as sources, correcting errors, using Google API calls (only) when errors are ambiguous, and identifying duplicates.
- Used PyTorch to build a BERT model to generate a sentiment measure from user-generated reviews of businesses and tourism sites. This measure allowed us to convert scores on a 1 – 5 score to a 1 – 100 score, improving the readability of the reviews.
- Used NLP and Spacy to extract keywords from reviews and identify important features of businesses and tourism sites. This improved tagging and provided new users with prompts for what to leave reviews about, improving site value.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Data Cleaning, Geography, PyTorch, Data Visualization, SpaCy, User Behavior, Analytics, Language Models, Sentiment Analysis, Machine Learning, Deep Learning, Data Pipelines, Neural Networks, APIs, Models, Modeling, EDA, Exploratory Data Analysis, Quantitative Analysis, Data Reporting, Minimum Viable Product (MVP), BERT, Reporting, Python 3, Data Science, Data Modeling, Jupyter Notebook, Data Scientist, Mathematics
Scientific Collaborator
2020 - 2021
School of Management, Fribourg
- Conducted extensive research on various trading algorithms with the aid of options data to determine which were the most robust over long-term investment periods.
- Trained a series of machine learning algorithms to see if they outperformed simpler trading algorithms and determined that their improvement was marginal at best.
- Oversaw the work of a PhD candidate and guided him in his research methods and report writing.
Technologies: Python 3, Python, MATLAB, Machine Learning, Algorithms, Sentiment Analysis, Predictive Analytics, Data Science, Predictive Modeling, Pandas, Operations Research, Data Modeling, Data Engineering, Mathematics, Applied Mathematics, Mathematical Finance, Mathematical Modeling, Mathematical Analysis, ETL, Data Analysis, Data Analytics, NumPy, Fintech, Scikit-learn, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Data Visualization, Linear Regression, Analytics, Financial Modeling, Stock Trading, Forecasting, Finance, Deep Learning, Data-driven Decision-making, Neural Networks, Models, Causal Inference, Modeling, EDA, Exploratory Data Analysis, Regression Modeling, Quantitative Analysis, Backtesting Trading Strategies, Regression, Data Reporting, Writing & Editing, Content Writing, Trading, Algorithmic Trading, Quantitative Research, Quantitative Modeling, Financial Analysis, Reporting, Quantitative Finance, Futures & Options, Statistical Methods, Time Series, Data Scientist, Statistical Analysis, Trading Strategy Development, Backtesting, Financial Markets, Risk Management
Data Scientist
2018 - 2021
iKentoo
- Built a tool to estimate waiting times in real-time for groups waiting for a table at any restaurant and at any time of day, using statistical modeling and Monte Carlo simulations, improving customer satisfaction for our clients.
- Trained an NLP model on our support tickets to facilitate auto-categorization, allowing our support staff more time to focus on clients' needs and improving efficiency.
- Built a revenue prediction model for restaurants whose prime feature was factoring in weather forecast data. Provided our clients with key information in regard to likely demand.
- Used advanced clustering and data cleaning techniques on our clients' communication preferences to help suggest which of our products would best suit our clients' businesses, significantly improving client satisfaction.
Technologies: Python 3, Python, Data Science, Predictive Analytics, Predictive Modeling, Pandas, Amazon Web Services (AWS), SQL, Operations Research, Data Modeling, Data Engineering, Mathematics, Applied Mathematics, Mathematical Modeling, Mathematical Analysis, ETL, Data Analysis, Data Analytics, NumPy, Hospitality, Scikit-learn, XGBoost, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Data Visualization, User Behavior, Linear Regression, Analytics, Google BigQuery, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Causal Inference, Language Models, Forecasting, Data Queries, PostgreSQL, BigQuery, Sentiment Analysis, Machine Learning, Deep Learning, Data Pipelines, Data-driven Decision-making, Neural Networks, MySQL, Natural Language Toolkit (NLTK), APIs, Models, Modeling, EDA, Exploratory Data Analysis, Regression Modeling, Quantitative Analysis, Regression, Data Reporting, Clustering Algorithms, Data Lakes, Minimum Viable Product (MVP), Quantitative Research, Quantitative Modeling, Reporting, Software Development, Clustering, Statistical Methods, Time Series, Jupyter Notebook, Data Scientist, Statistical Analysis, Backtesting
Head of End-user Engagement
2017 - 2018
Mandat International
- Created publicly available educational resources to promote citizen awareness and knowledge of IoT technology, Smart City interfaces, and the GDPR privacy legislation.
- Led the delivery of reports for the EU's Horizon 2020 program, focusing on end-user engagement.
- Created a series of multi-day educational workshops on smart cities and arranged for them to be broadcast online.
- Conducted research and analysis to compile a comprehensive report on stakeholders within the global cybersecurity ecosystem.
- Wrote and voiced several educational videos detailing the company's projects and outreach efforts.
Technologies: ISO Standards, Smart City Technology, Smart Cities, Cybersecurity, Educational Videos, Operations Research, Mathematics, User Behavior, Data Reporting, Writing & Editing, Content Writing, Reporting
Journalist
2016 - 2016
Dukascopy Bank
- Researched and interviewed important individuals from the world of business providing viewers with engaging profiles of them and their work.
- Researched and interviewed important individuals from the worlds of arts, culture, and sport, providing viewers with engaging profiles of them and their work.
- Researched and interviewed important individuals from the world of politics, providing viewers with engaging profiles of them and their work, including the CEO of the Brexit Vote Leave campaign, Matthew Elliott.
Technologies: Journalism, TV Broadcasting, Writing & Editing, Content Writing
Researcher
2011 - 2016
University of Geneva
- Designed and generated a sentiment measure from financial reporting and performed a time series analysis to determine whether this sentiment was priced in the stock market.
- Conducted causality inference tests to determine whether a particular form of sentiment is priced in the US stock market.
- Built an app in VBA for an academic economics ranking experiment. The app worked in English, French, and German and included well-calibrated randomized elements designed to maximize the information gained from the experiment's dataset.
- Utilized logistic regression models to determine how much of an observed economic outcome can be explained using aesthetic preferences.
- Developed a theoretical model of stochastic decision-making and the endowment effect to help explain pricing differences in two lottery valuation methods. This work won the 2016 SFI Best Doctoral Paper Award.
- Taught Asset Pricing Techniques to master's students at the University of Geneva.
Technologies: Pricing, Pricing Models, Option Pricing, Operations Research, Data Modeling, Data Engineering, Mathematics, Applied Mathematics, Mathematical Finance, Mathematical Modeling, Mathematical Analysis, Data Analysis, Data Analytics, Fintech, Text Mining, User Behavior, Linear Regression, Analytics, Financial Modeling, Stock Trading, Forecasting, Finance, Sentiment Analysis, Machine Learning, Decision Modeling, Data-driven Decision-making, Models, Causal Inference, Modeling, EDA, Exploratory Data Analysis, Regression Modeling, Quantitative Analysis, Backtesting Trading Strategies, Regression, Data Reporting, Writing & Editing, Content Writing, Teaching, Quantitative Research, Quantitative Modeling, Financial Analysis, Quantitative Finance, Forex Trading, Excel 2013, Futures & Options, Statistical Methods, Time Series, Data Scientist, Excel 365, Statistical Analysis, Predictive Modeling, Logistic Regression, Trading Strategy Development, Backtesting, Financial Markets, Risk Management
VBA Programmer
2009 - 2009
Groupe Renault
- Built a tool in Excel macro that forecasts component costs for vehicles based on statistical modeling of currency movements, providing the company with an important risk analysis tool.
- Built a tool in Excel macro with high flexibility in data representation and graph output, which was highly beneficial to the company's market analysts, who could easily see important sources of risk.
- Used high-level statistical modeling techniques to produce highly informative data for our market analysts in regard to risk stemming from forex trading movements.
Technologies: Excel VBA, Mathematics, Applied Mathematics, Mathematical Finance, Mathematical Modeling, Mathematical Analysis, Data Analysis, Data Analytics, PyTorch, Analytics, Financial Modeling, Forecasting, Models, Modeling, Exploratory Data Analysis, EDA, Quantitative Analysis, Financial Analysis, Forex Trading, Forex, Excel 2013, Statistical Methods, Excel 365, Statistical Analysis, Predictive Modeling, Financial Markets, Risk Management
Researcher
2007 - 2007
Objective Productions
- Created an outline for a TV pilot about weird facts about human behavior to sell to The Discovery Channel.
- Identified and presented key screen talent as possible presenters for my TV pilot.
- Worked closely with team members to outline the best possible episode topics and episode order for our submission to The Discovery Channel.
Technologies: Research, Film & Television, User Behavior, Writing & Editing, Content Writing
Experience
Estimated Waiting Times
I built an app in Python that predicted waiting times in real-time for groups waiting for a table at any restaurant at any time of day. This feature allowed our clients to increase customer satisfaction during busy periods.
Autofill on Support Tickets
To speed up the responsiveness of our customer support team, I built a script that auto-filled the support ticket categories using NLP. This allowed the support staff to concentrate on helping the client and increased the number of requests they could respond to.
Stock Market Sentiment Measure
As part of my PhD, I researched whether financial reporting describing the stock market as irrational had an effect on future stock market returns. I developed high-level skills in time series analysis, econometrics, and NLP in this project.
Smart City Education
I designed educational resources for the EU's Horizon 2020 rollout of smart city technology in several European test cities. The material focussed on IoT projects deployed at the municipal level, and I was responsible for the outreach to citizens affected by these technologies.
Foreign Exchange Projections
I built an app in Excel macros for a multinational car manufacturer that took exchange rate information for key currencies and automatically returned projected costs for components sourced from around the world. This allowed the company to take informed precautions on risk stemming from forex movements.
Behavioral Finance Research
In 2016, I won the Swiss Finance Institute's Best Paper Doctoral Award for my work on stochastic decision-making, the endowment effect, and its role in how individuals value lotteries and other assets with uncertain payoffs.
Review Keyword Identification
By aggregating user reviews and employing NLP, I was able to build a tool that identified key features of places of interest for tourists. This allowed us to cater prompts to the specific location, increasing the number of user reviews and boosting the overall usability of the platform.
Diverse Dataset Merging
I built a pipeline for taking business information from anywhere in the world and multiple sources that formatted the data into a schema, corrected errors, and identified duplicates. An important feature was that it minimized calls to the Google API when error-correcting.
Big Data Visualization
The first part of this project was to build matching SQL queries on two distinct telecoms datasets, each containing billions of entries. The second part was to design visualizations of the results, allowing the relevant team members to make important decisions regarding how the two datasets should ultimately be merged.
Feature Knockoff Construction
Using linear algebra, I designed a method for constructing Model-X knockoffs, a feature selection technique for machine learning models. This method produces pseudo-perfect knockoffs but is computationally expensive. I significantly reduced the time required to generate the pseudo-perfect knockoffs using creativity and efficient programming methods.
Modelling of Temperature Degradation
The client wanted to predict the theoretical upper or lower limit of the temperature in a given space while their machinery was either heating or cooling it. Since the target variable didn't exist, I devised a way to estimate the target variable using existing temperature readings in a time series. I used this to generate the target variable and devised a visualization method to see how temperature degradation rates changed over time by location.
Causal Inference Analysis
I completed a report for a marketing agency detailing the effect that increased advertising spend had on revenue. The dataset was very small and required a lot of cleaning. I was able to complete a full analysis of the data provided but had to advise the client that more data was needed in order to draw firm conclusions.
Sustainable Retirement Investing
I built a tool in Python that asks a retiree questions and then calculates the sustainability of their retirement portfolio over a given number of years, potentially decades. The tool simulates future stock and bond market movements and interest rates using historical data and Monte Carlo techniques.
Trading Algorithm Backtesting
I completed a report for the Swiss government on the risk profiles of various trading algorithms for long-term investing (pensions). The analysis included data from a market maker in options and futures as well as some experimental trading strategies using machine learning.
Skills
Languages
Python 3, Python, SQL, Excel VBA, R, JavaScript
Libraries/APIs
Pandas, NumPy, Scikit-learn, SpaCy, Natural Language Toolkit (NLTK), XGBoost, PyTorch, SciPy, TensorFlow
Paradigms
Data Science, ETL, Quantitative Research
Storage
PostgreSQL, Data Pipelines, MySQL, NoSQL, Data Lakes, Google Cloud
Industry Expertise
Trading Strategy Development, Teaching, Cybersecurity, Marketing
Other
Capital Asset Pricing Model (CAPM), Pricing, Pricing Models, Price Analysis, Pricing Strategy, Derivative Pricing, Econometrics, Behavioral Economics, Probability Theory, Stochastic Modeling, Statistics, Statistical Methods, Statistical Analysis, Statistical Modeling, Statistical Data Analysis, Time Series, Time Series Analysis, Machine Learning, Algorithms, Option Pricing, Research, Data Modeling, Data Engineering, Mathematics, Applied Mathematics, Mathematical Finance, Mathematical Modeling, Mathematical Analysis, Data Analysis, Data Analytics, Linear Regression, Analytics, Financial Modeling, Causal Inference, Forecasting, Decision Modeling, Data-driven Decision-making, Models, Modeling, EDA, Exploratory Data Analysis, Monte Carlo Simulations, Regression Modeling, Quantitative Analysis, Backtesting Trading Strategies, Regression, Data Reporting, Minimum Viable Product (MVP), Quantitative Modeling, Financial Analysis, Reporting, Data Scientist, Backtesting, Stochastic Differential Equations, Bayesian Statistics, Statistical Programming, Statistical Learning, Natural Language Processing (NLP), Clustering, Sentiment Analysis, Predictive Analytics, Predictive Modeling, Film & Television, TV Broadcasting, Operations Research, Fintech, Hospitality, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Text Mining, Data Visualization, Physics, User Behavior, Google BigQuery, Stock Trading, GPT, Generative Pre-trained Transformers (GPT), Optimization, Linear Optimization, Language Models, Data Queries, Finance, Deep Learning, Big Data, Neural Networks, APIs, Writing & Editing, Content Writing, Trading, Algorithmic Trading, Clustering Algorithms, Applied Physics, Quantitative Finance, Software Development, Futures & Options, Excel 365, Logistic Regression, Financial Markets, Risk Management, Behavioral Science, Neuroscience, ISO Standards, Smart City Technology, Smart Cities, Educational Videos, Journalism, Internet of Things (IoT), Data Cleaning, Geography, Networking, Financial Data, Variational Autoencoders, BERT, Forex Trading, Forex, Reports
Tools
STATA, BigQuery, Excel 2013, MATLAB, Amazon Athena
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Google Cloud Platform (GCP)
Education
2011 - 2017
Ph.D. in Finance
University of Geneva - Geneva, Switzerland
2003 - 2007
Master's Degree in Mathematics
Imperial College - London, England, UK
Certifications
SEPTEMBER 2023 - SEPTEMBER 2025
Professional Data Engineer
Google Cloud
SEPTEMBER 2022 - PRESENT
JavaScript Basics for Beginners
Udemy
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