Leandro J M Machado，巴西圣卡塔琳娜州Florianópolis开发者
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Leandro J M Machado

Verified Expert  in Engineering

Machine Learning Operations (MLOps) Developer

Location
Florianópolis - State of Santa Catarina, Brazil
Toptal Member Since
October 4, 2018

Leandro is a data scientist and since 2013, 他一直在为电子商务领域的客户提供数据和机器学习解决方案, advertising, insurance, and financial services. 他的专业范围从早期原型设计到部署大规模机器学习服务.

Data AnalyticsMachine LearningRecommendation SystemsPythonElasticsearchSparkDeep LearningCloud FoundryNatural Language Processing (NLP)Amazon Web Services (AWS)DockerFlaskNumPyAmazon S3 (AWS S3)Apache Kafka

Portfolio

Vality
Amazon Web Services (AWS), TensorFlow, Python
Capco
Gemfire, Solr...
Chaordic
Redis, Cassandra, Apache Kafka, Elasticsearch, Amazon S3 (AWS S3)， Amazon EC2...

Experience

Machine Learning Operations (MLOps) - 10 yearsMachine Learning - 10 yearsData Analytics - 10 yearsPython - 10 yearsCredit Modeling - 5 yearsDeep Learning - 5 yearsFraud Prevention - 5 yearsSpark - 5 years

Availability

Part-time

Preferred Environment

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

The most amazing...

...我帮助巴西最大的银行之一从零开始建立了一个数字银行.

Work Experience

Lead Scientist

2017 - PRESENT
Vality
  • Developed and maintained a credit-and-fraud analysis pipeline.
  • 编写风险、遵从性、投资组合管理和定价政策.
  • 建立并支持一个基于bureau的私有信用评分系统, purchase, and social network data.
  • Mapped and integrated data providers.
  • Led teams.
Technologies: Amazon Web Services (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.
  • 领导开发Next Bank移动应用的自助帮助解决方案. 该模型基于TF-IDF信息检索，采用汤普森采样进行反馈增强.
  • 管理Next Bank手机应用的消费限额推荐解决方案的开发. 该模型基于用户-用户协同过滤，使用帐户和信用卡数据，并采用线性回归模型处理冷启动.
  • 领导开发Next Bank移动应用的实时报价推荐解决方案. 该模型必须实时跟踪客户交易，根据客户偏好和符合条件的合作伙伴推荐个性化的优惠和津贴.
Technologies: Gemfire, Solr, VMware Tanzu Application Service (TAS) (Pivotal Cloud Foundry (PCF)), Hadoop, Apache Kafka, Apache Hive, Spark, Python

Big Data Scientist

2013 - 2016
Chaordic
  • Researched and developed recommendation algorithms for eCommerce.
  • 开发跨店广告实时产品推荐服务. 该模型使用客户当前感兴趣的列表来推荐其他在线商店中更便宜的类似产品. 该解决方案还包括一个拍卖系统的设计报价安置投标.
  • 建立广告实时竞价系统，实现广告自动化，提高广告客户的投资回报率. 该模型考虑了客户的导航和购买历史来估计最优出价.
  • 创建一个实验平台，以简化A/B测试的评估和部署. 该平台帮助将进行在线实验的时间和成本减少了一半.
Technologies: Redis, Cassandra, Apache Kafka, Elasticsearch, Amazon S3 (AWS S3)， Amazon EC2, Spark, Scala, Python

Advice for Applying Machine Learning

http://medium.com/@leandromachado_11293/from-magic-to-method-advices-for-applying-machine-learning-fb363136e786
我写了一篇文章，解释了如何有效地将机器学习应用于工业问题.

Consumer Spending Limit Recommendation

我为一家数字银行设计并领导了一个推荐系统的实施，该系统帮助客户为17种交易类别中的每一种设定最佳支出限制.

该解决方案是用Scala/Spark构建的，并利用了tb级的客户信用卡和支票账户交易数据. 采用线性模型和用户-用户协同过滤相结合的方法, the solution was robust enough for all classes of customers.

Real-time Credit Analysis Pipeline

我设计并领导了一个在线消费者融资解决方案的信用和欺诈分析算法的实现.

该解决方案是用Python实现的，并利用来自征信机构和其他替代数据源的数据来有效地评估客户的信誉度. 欺诈分析步骤包括面部识别，并将提供的信息与社交网络等在线声誉来源进行匹配. With this combination of technologies, 我们可以为客户提供快速轻松的入门流程，同时实现KS和Gini的以上市场指标，以进行默认预测.

Experimentation Platform

我设计并领导了a /B测试实验高效分析工具的实现.

它由一个Scala/Spark作业组成，该作业从几个来源(比如印象)获取输入, clicks, purchases, 以此类推，然后处理这些信息，输出一些统计数据和假设检验. 该方法包括实现小引导袋t检验，以进行鲁棒性和计算效率分析. It reduced the time and cost of running experiments by half.

Recommendation System for Real-time Advertisements in eCommerce

设计并主导实现了跨店广告实时推荐系统.

该服务是用Scala实现的，并使用了诸如product-content之类的变量, product-CTR/Conv, 并根据客户的交易历史向客户推荐同类产品. 该解决方案是用Scala构建的，并使用来自Elasticsearch等数据库的数据, Cassandra, and Redis. 它还为30多家广告商提供服务，并被部署在巴西三大电子商务公司之一.

Languages

Python, Scala

Frameworks

Spark, Flask, Hadoop

Libraries/APIs

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

Platforms

Jupyter Notebook, Amazon Web Services (AWS), Docker, Apache Kafka, Amazon EC2, VMware Tanzu Application Service (TAS) (Pivotal Cloud Foundry (PCF))

Storage

Elasticsearch, Amazon S3 (AWS S3), Apache Hive, Cassandra, Redis

Other

Recommendation Systems, Advertising, Data Analytics, Machine Learning, Machine Learning Operations (MLOps), Credit Risk, eCommerce, Gemfire, Cloud Foundry, Natural Language Processing (NLP), Social Networks, Credit Modeling, Fraud Prevention, Deep Learning, GPT, Generative Pre-trained Transformers (GPT)

Tools

Solr, GitHub

2009 - 2013

Bachelor of Science Degree in Computer Science

Universidade Federal de Santa Catarina - Florianópolis, Brazil

2012 - 2012

Completed Credits in Computer Science

University of Virginia - Charlottesville, VA, USA

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