Hi, I am Deshawn Sambrano. I am a Data Scientist/Applied Scientist with a passion for solving problems with data. I received my PhD from Harvard where I studied Economics, Psychology, and Neuroscience. My dissertation focused on consumer choices and designing A/B tests to modify their decisions. Since 2022, I have been consulting for Activision on their Call of Duty Franchise designing machine learning models, and implementing A/B testing that went out globally to millions of users to modify in game behaviors. Here are a few different project that demonstrate my versatility as a Data Scientist, Applied Scientist, and Machine Learning Researcher.
- Activision/Call of Duty
- A/B Test Optimization
- Behavior Modification
- Forecasting Consumer Choices
- Biostatistics
- Bayesian Statistics
- Interactive Demos
⭐Executive Summary📈: Collaborating with Activision on their renowned game Call of Duty, this project targets the reduction of toxic behavior and the promotion of prosocial interactions among players. Leveraging a combination of traditional analytical methods and cutting-edge machine learning models, we have developed and fine-tuned strategies to enhance the gaming environment. The core of this initiative involves designing and implementing A/B tests, set to roll out globally in the upcoming weeks. These tests are meticulously crafted to assess the effectiveness of various interventions in real-time, aiming to foster a more positive and engaging community within the game. This initiative not only improves the player experience but also sets a precedent for using advanced data-driven techniques to cultivate healthier online communities in the gaming industry.
⭐Executive Summary📈: Running and evaluating the effectivness of A/B tests is costly and some users may not like the changes. Here, I designed a alternative design to evaluate new models with A/B test, which can achieve the same model accuracy in 3/4th the time or increase the model accuracy by more than a factor of 4 in the same amount of time. By using my adaptive design, you can have more efficiently structured testing.
⭐Executive Summary📈: Changing consumer behavior is challenging and costly. Here, I show how precisely controlling what is show to the consumer can yield larger changes in consumer behavior with lower costs! Small but precisely curated emotional inductions can be more impactful to consumer decisions than large scale manipulations.
A behavior modification A/B test designed to evaluated consumer decisions based on their emotional state.
⭐Executive Summary📈: Understanding and predicting consumer choices is pivotal for strategic business decision-making. This project presents a comprehensive model that accurately predicts consumer behavior by analyzing various market trends and psychological factors. By integrating advanced analytics with consumer psychology, this model offers a powerful tool for businesses to forecast consumer preferences, optimize product offerings, and enhance marketing strategies, ultimately leading to more informed decisions and increased profitability. This model stands as a breakthrough in the realm of consumer choice modeling, providing a robust framework for anticipating market dynamics and tailoring business approaches to meet evolving consumer needs.
For this project, I created a custom machine learning model to forecast risky decision making for lotteries. These analyses were presented in front a large mixed audience and published here. The first plot shows that custom ML model used to forecast consumer decisions. Specifically, we highlight how we precisely modeled the specific features use to make financial decisions on a per user basis!
⭐Executive Summary📈: Collecting eyetracking data and other physiological measures from consumers near the time of viewing ads can yield strong forecasts for future consumer choices.
Below is another plot where we highlighted the relationship between the bodily characteristics and consumer choices. Specifically, we evaluated eyetracking measures (as well as skin sweat, blood pressure, hormone levels and heart rate; not shown)
This project demonstrates my ability to use and easily explain Bayesian Statistics. Specifically, these plots show how the posterior (the final prediction of the model) changes as you adjust the prior (the background knowledge of the model).
As show with the image on the left, if you don't give your model the proper prior (background knowledge of what is likely to happen) it can be overconfident which can lead to inaccurate predictions and lost of revenue. In contrast, the plot on the right has a good prior making it more robust to random fluctuations and produces much more stable results yielding more accurate forecasts.
Below are a couple on live data visualization and analysis demonstrations. You are given some simulated data and as you adjust the different parameters of the dataset you can see how it changes that data visually as well as from a statistical standpoint. These demonstrations were used to provide some statistical intuitions for Intro Statistics Student's for a Course I taught at Harvard University.