Leandro J M Machado, Software Developer in Florianópolis - State of Santa Catarina, Brazil
Leandro J M Machado

Software Developer in Florianópolis - State of Santa Catarina, Brazil

Member since August 8, 2018
Leandro is a data scientist and since 2013, he's been delivering data and machine learning solutions for clients in the fields of eCommerce, advertising, insurance, and financial services. His expertise ranges from early-stage prototyping to deploying large-scale machine learning services.
Leandro is now available for hire

Portfolio

  • Vality
    Amazon Web Services (AWS), AWS, TensorFlow, Python
  • Capco
    Gemfire, Solr, Pivotal Cloud Foundry (PCF), Hadoop, Apache Kafka, Apache Hive...
  • Chaordic
    Redis, Cassandra, Apache Kafka, Elasticsearch, AWS S3, AWS EC2, Spark, Scala...

Experience

Location

Florianópolis - State of Santa Catarina, Brazil

Availability

Part-time

Preferred Environment

Amazon Web Services (AWS), GitHub, AWS, Docker, Spark, Scala, Python

The most amazing...

...thing I've helped to build was a digital bank from scratch for one of the largest banks in Brazil.

Employment

  • Lead Scientist

    2017 - PRESENT
    Vality
    • Developed and maintained a credit-and-fraud analysis pipeline.
    • Wrote risk, compliance, portfolio management, and pricing policies.
    • Built and supported a proprietary credit score system based on bureau, purchase, and social network data.
    • Mapped and integrated data providers.
    • Led teams.
    Technologies: Amazon Web Services (AWS), AWS, TensorFlow, Python
  • Data Scientist | Delivery Lead

    2016 - 2017
    Capco
    • Developed and managed the delivery of analytics solutions for Bradesco's Next Bank; including project scoping, planning, reporting, and risk management.
    • Led the development of a self-service help solution for Next Bank's mobile app. The model was based on TF-IDF information retrieval with feedback boosting using Thompson sampling.
    • Managed the development of a spending limit recommendation solution for Next Bank's mobile app. The model was based on user-user collaborative filtering using account and credit card data with a linear regression model to handle a cold start.
    • Led the development of a real-time offer recommendation solution for Next Bank's mobile app. The model had to track customer transactions in real-time to recommend personalized offers and perks based on customer preference and eligible partners.
    Technologies: Gemfire, Solr, Pivotal Cloud Foundry (PCF), Hadoop, Apache Kafka, Apache Hive, Spark, Python
  • Big Data Scientist

    2013 - 2016
    Chaordic
    • Researched and developed recommendation algorithms for eCommerce.
    • Developed a real-time product recommendation service for cross-store advertisement. The model used a customer's list of current interests to recommend similar products that are cheaper in other online stores. The solution also included a design of an auction system for offer placement bidding.
    • Built a real-time bidding system for advertisements in order to automate and increase the ROI for the advertiser. The model considered a customer's navigation and purchase history to estimate the optimal bid price.
    • Created an experimentation platform to streamline the evaluation and deployment of A/B tests. The platform helped reduce—by half—the time and costs of conducting online experiments.
    Technologies: Redis, Cassandra, Apache Kafka, Elasticsearch, AWS S3, AWS EC2, Spark, Scala, Python

Experience

  • Advice for Applying Machine Learning
    https://medium.com/@leandromachado_11293/from-magic-to-method-advices-for-applying-machine-learning-fb363136e786

    I wrote an article that gave explanations on how to efficiently apply machine learning to industry problems.

  • Consumer Spending Limit Recommendation

    I designed and led the implementation of a recommendation system for a digital bank that helped its customers set optimal spending limits for each of the 17 transaction categories.

    The solution was built in Scala/Spark and leveraged terabytes of customer credit card and checking account transaction data. By using a combination of linear models and user-user collaborative filtering, the solution was robust enough for all classes of customers.

  • Real-time Credit Analysis Pipeline

    I designed and led the implementation of credit-and-fraud analysis algorithms for an online consumer financing solution.

    The solution was implemented in Python and leveraged data from credit bureaus and other alternative data sources to efficiently evaluate customer creditworthiness. The fraud analysis step included facial recognition and matched the provided information with online sources of reputation such as social networks. With this combination of technologies, we could provide a fast-and-light onboarding process for the customer while achieving above market metrics of KS and Gini for default prediction.

  • Experimentation Platform

    I designed and led the implementation of a tool for efficient analysis of A/B test experiments.

    It consisted of a Scala/Spark job that took input from several sources—such as impressions, clicks, purchases, and so on—and processed this information to output a number of statistics and hypothesis tests. The methodology included an implementation of the Bag of Little Bootstraps T-test for a robust-and-computationally-efficient analysis. It reduced the time and cost of running experiments by half.

  • Recommendation System for Real-time Advertisements in eCommerce

    I designed and led the implementation of a real-time recommendation system for cross-store advertisement.

    The service was implemented in Scala and made use of variables such as product-content, product-CTR/Conv, and the customer's transaction history to recommend similar products. The solution was built in Scala and consumed data from databases such as Elasticsearch, Cassandra, and Redis. It also served over 30 advertisers and was deployed on one of the top three eCommerces in Brazil.

Skills

  • Languages

    Scala, Python
  • Frameworks

    Spark, Hadoop, Flask
  • Libraries/APIs

    Spark Streaming, Scikit-learn, NumPy, TensorFlow, spray
  • Platforms

    Jupyter Notebook, Apache Kafka, AWS EC2, Pivotal Cloud Foundry (PCF), Amazon Web Services (AWS), Docker
  • Storage

    Elasticsearch, AWS S3, Apache Hive, Cassandra, Redis
  • Other

    Recommendation Systems, Advertising, Credit Risk, eCommerce, Gemfire, CloudFoundry, Natural Language Processing (NLP), Social Networks, AWS
  • Tools

    Solr, GitHub

Education

  • Bachelor of Science degree in Computer Science
    2009 - 2013
    Universidade Federal de Santa Catarina - Florianópolis, Brazil
  • Completed credits in Computer Science
    2012 - 2012
    University of Virginia - Charlottesville, VA, USA

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