Lead Developer
2018 - PRESENTPinTecnologia- Built a system to process administrative documents over the Ethereum blockchain.
- Worked on the front-end and built the UI using React.
- Designed and developed the back-end using Django and the Django REST framework.
Technologies: Blockchain, Ethereum, React, DjangoProduct Owner
2017 - PRESENTBMD Software- Managed the PACScenter product which is an all-in-one medical imaging platform for patient studies (storage, visualization, and sharing)—enabling simple and efficient workflows.
- Developed core features for both the back-end and front-end.
- Defined the strategy for new version releases.
Technologies: MySQL, JavaScript, HTML, Hibernate, JPA, Scala, Java, Play FrameworkKaggle Competitions Expert
2018 - 2018TalkingData AdTracking Fraud Detection- Worked with a large dataset using big data tools like Apache Spark.
- Built an algorithm that predicts whether a user will download an app after clicking a mobile app ad which helps to combat click fraud.
Technologies: Feature-driven Development (FDD), Gradient Boosting, MLlib, Apache SparkKaggle Competitions Expert
2018 - 2018Toxic Comment Classification- Studied negative online behaviors e.g., toxic comments.
- Built a multi-label model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate.
- Used a labeled dataset of comments from Wikipedia’s talk page edits.
Technologies: Support Vector Machines (SVM), Naive Bayes, Ensemble Methods, Gated Recurrent Unit (GRU), Embedded Development, GloVe, LSTM, Deep LearningKaggle Competitions Expert
2018 - 2018Recruit Restaurant Visitor Forecasting- Predicted how many customers to expect in each day in a restaurant to effectively purchase ingredients and schedule staff members.
- Developed a prediction model for this task; this was not easy to make because many unpredictable factors can affect restaurant attendance like the weather and local competition. It's even harder for newer restaurants with little historical data.
- Worked with heterogeneous datasets.
Technologies: Gradient Boosting, Time Series, ARIMA, LSTMKaggle Competitions Expert
2017 - 2017Sberbank Russian Housing Market- Created a prediction model capable of making predictions about realty prices so that renters, developers, and lenders are more confident when they sign a lease or purchase a building.
- Developed algorithms which use a broad spectrum of features to predict realty prices, using a rich dataset that includes housing data and macroeconomic patterns.
Technologies: Validation, Feature-driven Development (FDD), Ridge Regression, Gradient BoostingKaggle Competitions Expert
2017 - 2017Two Sigma Financial Modeling- Applied technology and systematic strategies to financial trading in order to forecast economic outcomes that can never be entirely predictable,.
- Back-tested to validate regression models that predict financial time series.
Technologies: Ridge Regression, ExtraTreesRegressor, TensorFlow, Reinforcement LearningKaggle Competitions Expert
2016 - 2016Santander Customer Satisfaction- Created a model that identifies dissatisfied customers.
- Worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Technologies: Principal Component Analysis (PCA), Neural Networks, Ensemble Methods, Decision Trees, Random ForestsKaggle Competitions Expert
2014 - 2014Loan Default Prediction- Determined whether a loan will default, as well as the loss incurred if it does default.
- Developed methods unlike traditional finance-based approaches to this problem, where one distinguishes between good or bad counter parties in a binary way, we sought to anticipate and incorporate both the default and the severity of the losses that result.
- Built, as a team, a bridge between traditional banking, where we are looking at reducing the consumption of economic capital, to an asset-management perspective, where we minimized the risk to the financial investor.
Technologies: Gradient Boosting, Random ForestsKaggle Competitions Expert
2013 - 2013Job Salary Prediction- Built a prediction engine for the salary of any UK job advertisement so they can make huge improvements in the experience of users searching for jobs, and help employers and job seekers figure out the market worth of different positions.
- Worked with a large dataset (hundreds of thousands of records) which was mostly unstructured text with few structured data fields. These were in a number of different formats because of the hundreds of different sources of records.
Technologies: Dimensionality Reduction, Tf-idf, Natural Language Processing (NLP), Ridge Regression, Random Forest Regression