Machine learning engineer with 5 years of experience and a strong track record of implementing statistical machine learning and deep learning solutions to improve business performance. Key achievement: Pioneered the inte…
Machine learning engineer with 5 years of experience and a strong track record of implementing statistical machine learning and deep learning solutions to improve business performance. Key achievement: Pioneered the integration of machine learning models into core operations, driving a 70% increase in customer retention with a churn prediction model, optimized cash flow by 60% through deposit forecasting and boosted fraud detection accuracy by 80% in sports betting
Online gaming company based out of Malta offering sports betting, casino, and live casino experience.
● Successfully pioneered the integration of 6 machine learning models into core business operations
● Used EvalML to develop churn prediction model and anticipate churn rates based on varying activity
lengths and identify players at risk of churn to inform targeted engagement increasing retention by 70%
● Leveraged XGBoost to construct a predictive model, forecasting deposits for a 90-day period based on initial 7 days of activity helping strategic player acquisition decisions and resulting in a substantial 60% increase in revenue generation opportunities
● Employed RandomForest algorithm to detect sports fraudsters based on initial 10 sports bets helping to bolster platform security and trust, successfully reducing false negatives by 80% as a direct result
<p>Successfully pioneered the integration of 6 machine learning models into core business operations<br>
Used EvalML to develop churn prediction model and anticipate churn rates based on varying activity lengths and identify players at risk of churn to inform targeted engagement increasing retention by 70%<br>
Leveraged XGBoost to construct a predictive model, forecasting deposits for a 90-day period based on initial 7 days of activity helping strategic player acquisition decisions and resulting in a substantial 60% increase in revenue generation opportunities<br>
Employed RandomForest algorithm to detect sports fraudsters based on initial 10 sports bets helping to bolster platform security and trust, successfully reducing false negatives by 80% as a direct result</p>
<p>Managed entire lifecycle of Machine Learning models including data analysis and pipeline development<br>
Analyzed/preprocessed bank transaction data to be used in the creation of fraud detection models<br>
Developed and hyper-tuned supervised ML models with Feedzai to detect cross-channel fraud with an accuracy of 80%<br>
Ensured model precision and identified opportunities for improvement through weekly model evaluation<br>
Implemented an Airflow DAG to automate the process of generating training and test datasets for modeling (involves data extraction from Redshift using custom SQL scripts, transferring data to an AWS S3 bucket, preprocessing with SageMaker and uploading back to AWS), this streamlined workflow not only reduced manual effort by 50% but also ensured timely execution triggered by Airflow</p>
<p>Researched, designed, and deployed over 30 machine learning models from initial raw data analysis to preprocessing to ensure data was appropriate for modeling and of sufficient quality<br>
Visualized dataset with over 2,000 features to identify trends/patterns using PCA and performed feature engineering to identify the most prominent features for modeling<br>
Built deep learning models using TensorFlow and Keras to predict pipeline flow and pressures achieving MAE of +-5 with 95% accuracy<br>
Incorporated models with optimization module using Pyomo reducing operational expenses by 60%<br>
Conducted comprehensive testing on over 20 edge cases to evaluate model performance and worked on an object detection module for computer vision project</p>
<p>Researched various trading frameworks such as Tensortrade and AI4 to stay up-to-date on the industry<br>
Maintained strong knowledge of cryptocurrency trading strategies, technology, and market dynamics<br>
Identified key features responsible for stock price movement, modeled a predictor to predict stock price direction using transformer, gan, and lstm yielding an impressive 82% accuracy rate<br>
Utilized TensorTrade an open source deep reinforcement learning framework as the foundation for developing customized trading strategies<br>
Applied reinforcement learning algorithms, including DQN and PPO, with the Sortino ratio as the primary reward metric, to create effective trade strategies with a sortino ratio of 2.5</p>
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