The newsletter for continuous learning on Machine Learning applications, Machine Learning System Design, MLOps, and the latest techniques and news about the field. https://newsletter.theaiedge.io/

I specialize in building large scale end to end Machine Learning capabilities. I can help you assess the AI readiness of your data and data infrastructures. I can architect the solutions, set KPIs, timelines, milestone and help in establishing a budget and headcount. I can train the models, deploy them and architect the infrastructures to support the Machine Learning solutions. I can coordinate the teams collaborations and train the teams as needed to maximize the success of the endeavors.

I lead a small team of Data Scientists, Data Engineers and Software Engineers for a new Data Science initiative in the Private Cloud Department. I have assessed the business inefficiencies and designed Machine Learning solutions applied to customer ticket routing and failure predictions.

I am heading the Data Science initiative at OPSkins. I have built Machine Learning models for pricing optimization, product recommendation and Natural Language Processing. I have identified the deployment patterns for the Machine Learning products and I was leading the effort to construct a data warehouse for data analytics.

I have joined Bluestem as a Senior Data Scientist to partake in the brand-new Data Science initiative that has been key for the company in generating $2B in annual revenue. This includes identifying inefficiencies and building predictive models for business optimization across domains such as marketing, credit analysis or sales. I have had the opportunity to develop and deploy numerous models using machine learning techniques and big data technologies. Some of my accomplishments are: • Increasing by 10% the new customer acquisition rate through mailing campaigns using binary classification algorithms. • Enhancing customer experience with personalized product recommendation during call offers.• Improving sales profit with price optimization across ~200,000 different products and inventory forecast.• Developing new ECOA and FCRA compliant Machine learning techniques for adverse actionable credit worthiness prediction and credit line assignment. • Deploying highly accurate sales forecast software for promotion campaigns.

I have joined the California State University-Long Beach faculty to teach master students about Data Science. They have learned about Python programming, statistical inference and Machine Learning. We have explored different subjects such as Natural Language Processing, Monte Carlo simulations, Image analysis and Deep Learning. It has been a truly enriching experience to be able to guide students to become data scientists!

I have worked in a research team applying machine learning techniques to optimize products and create new technological advances:• I have developed predictive models for HDD disks failure and file systems crash (R, python). I have extracted, analyzed and modeled data from a multi-terabyte Greenplum relational database (PostgreSQL) and from a Hadoop file system (PIG).• I have designed new methods of feature selection for non-linear mixed data with a high number of missing values using information theory techniques.• I have designed new semi-supervised techniques of clustering to unmix data.• I have applied Hidden Markov Modeling to backup file systems data labeling.

• Developed machine learning algorithms for optimization of blood management (Java, R, Python, Hadoop). • Developed data visualization tool for business intelligence (D3.js, Google App Engine, SQL). • Developed iOS and Android mobile apps (Objective C, Android, AWS, DynamoDB, S3).

• Development of Energy dispatch algorithm integrated into a Monte Carlo Machine for energy valuation (C++, Python/Cython). • Development of derivatives pricing methods in energy markets Development and enhancement and maintenance of stochastic models for demand and price, network congestion, gas storage and transportation.

• Development of Monte Carlo machines for stochastic differential equations and statistical physics. • Data mining and treatment of high dimensional data in a multi-terabytes database cluster • Testing of statistical arbitrage strategies using machine learning techniques for equity trading. • Development of diffusion, stochastic and probabilistic models for turbulent flows. • Study of algebraic models for quantum matter. (C/C++, Matlab, Fortran)