Resume from Diego Marinho - Data Scientist Manager. Last update on Oct, 2020.

1. “Environment Thinking - Did you know that this resume was optimized to use less amount of printer ink?”
Melbourne, Victoria, Australia Diego Marinho de Oliveira
https://www.linkedin.com/in/dmztheone
dmarinho.ai@gmail.com
Professional Experience
Data Scientist Manager
Senior Data Scientist
Data Scientist
SEEK
Since March 2019
June 2018 to March 2019
March 2017 to June 2018
SEEK is a global market leader in online employment and has the potential to reach 2.9 billion people around the globe. As part of the
SEEK Group, I played different roles in the company. I started as a Data Scientist (DS) and after as a Senior DS leading many projects
of Online Recommender Systems and Personalised Search in different markets in Asia and Australia/New Zealand. I dealt with many
technologies associated with Machine Learning, Natural Language Processing, Information Retrieval, Real-Time Systems on Cloud,
Unified Data Layer and Data Pipelines to deliver the best AI that adapts dynamically to enhance candidates and hirers experience.
Since 2019, as AI Manager, I led a large team of global Data Scientists (in different countries) that collaborate to deliver exceptional
results improving the AI at the company. During this time, I had an essential role in the transition about how Recommendations can
have a substantial impact in users daily life helping them to find the right content at the right time in a given context.
In summary, as part at SEEK I accumulated many successfully delivered projects in almost 4 years, all measured through A/B testing
and promoting diverse and steady development of new leaders for the company.
Lead Data Scientist Catho Online July 2015 to February 2017
Catho is the largest employment website in Brazil and is part of SEEK international group. As part of the innovation group at
CathoLabs, I was responsible for creating a novel Recommender System (RS) products aligned with real business metrics. In
particular, the RS required the usage of Natural Language Processing algorithms to solve real problems, perform complex analysis and
apply Machine Learning techniques for prediction and classification in non-trivial tasks. Thus, as an additional challenge, the daily
activities involved working with an extensive amount of data and adopt Big Data solutions that scale and that represents the state-of-
the-art algorithms. Overall, all projects were measured using A/B Testing, and many of them were famous by delivering significant
uplift in the primary metrics of the marketplace, ensuring that our AI was a strategic part of the business to enhance the candidate and
hirers experience.
Machine Learning Engineer RBS Group - Appus Startup December 2013 to June 2015
RBS Group is a telecommunication company that its main businesses are TV, radio and newspaper. In one of its startups (Appus),
the company was venturing on well-skilled employees that can create analytical solutions from a pool of information. Thus, as
professional, in late 2013 I was already proposing the usage of the latest technologies for Big Data as Map-Reduce (Hadoop, Apache
Spark), NoSQL databases (ElasticSearch, MongoDB, Redis), cloud computing (Amazon Web Service Cloud and Google Cloud),
high-performance scientific programming languages (Python, Java, Scala, C, C++ and R) to develop complete solutions for
simulations, forecasting, classification, data clusterization and text information extraction.
Machine Learning Specialist Zunnit Technologies April 2013 to October 2013
Software Engineer and Machine Learning specialist working in leading technologies (Java and Python) to develop state of art
recommendation services based on content. Specifically, the team was composed of the most skilled professionals of Computer
Science, led by professor and PhD Nivio Ziviani, one of the top researchers in Information Retrieval and the one who was involved
in the sale of Akwan to Google in 2005. In particular, the role was focused on build and maintained Online Recommender Systems
for e-commerce (Peixe Urbano) and news websites from Globo group.
Software Engineer Visagio July 2012 to April 2013
I worked as a Software Engineer in projects inside of the world’s largest company producer of iron ore and pellets (Vale mining
company). The project encompassed the development of an internal real-time Web system in Java with Google Web Toolkit
(GWT) to manage the supply of a determined department inside of the company.
Software Analyst TOTVS May 2009 to June 2010
First experience outside of academia after a long period in the laboratory and student monitoring. It was a unique experience
working in a prestigious company in Brazil. The objective was to learn global lead techniques for the development of software
products. At the time, my primary responsibility was to develop solutions for the HR section (i.e., employee time management
and payroll) using the latest .NET technology from Microsoft.

2. Education
Master of Science in Computer Science Universidade Federal de Minas Gerais 2010 to 2012
Master of Science focused in Software Engineer (1st
year) and after changed to Natural Language Processing and Probabilistic Models
for Named Entity Recognition (NER). As a result, I published a novel probabilistic technique for NER that the paper was listed in top
10 for a few years in some researcher lists specialized on the subject.
Bachelor of Science in Computer Science Pontifícia Universidade Católica de Minas Gerais 2006 to 2009
Bachelor of Science focused in a hybrid curriculum between applied mathematics (monitoring and scientific work), machine learning
through Artificial Neural Networks on my lab and some internships in the industry to gain experience at the practical side (TOTVS
company). During this time published 3 papers (two related to ANN and one associated with Software Engineering). As a high note,
achieved high grades and received an award as best 2nd student in the class.
Awards
1st
Winner of the Volponi Awards SEEK Australia – Distinct AI work contribution in Asia.
Top 2nd
student award. Honour by high-grade achievement during my BSc. in Computer Science.
August 2017
December 2009
Publications
FS-NER: A Lightweight Filter-Stream Approach to Named Entity Recognition on Twitter Data in the proceedings of the 3rd
Making Sense of Microposts conference at WWW Workshop held in Rio de Janeiro, RJ – Brazil 13 to 17 May 2013.
Applying Neural Networks to Determine the Socio-Environmental Factors Responsible for Potable Water Consumption at
the System, Man and Cybernetics (SMC) held in Istanbul - Turkey 10 to 13 October 2010.
The Usage of Artificial Neural Networks in the classification and forecast of portable water consumption at the International
Joint Conference on Neural Networks (IJCNN) held in Atlanta, GE – USA 14 to 19 July 2009.
New Perspectives in the Context of Computer Architecture – Educational Software for the Paradigm of Parallel
Computation at the Workshop sobre Educação em Arquitetura de Computadores (WEAC) held in São Paulo, SP - Brazil 28
October 2009.
Volunteer
Volunteer for promoting Julia language between Data Scientists, since 2015.
Volunteer at Python Software Foundation promoting, protecting and advancing the Python programming language, 2014.
Presentation of Lecture on Artificial Neural Networks for students of Computer Science, 2007.
Knowledge and Skills
Main Fields:
• Information Retrieval
• Recommender Systems
• Machine Learning
• Natural Language Processing
• Deep Learning / Artificial Neural Networks
• Artificial Intelligence
• Data Mining
• Data Visualisation
• Data Science
• Statistics, Probability and Math
ML Techniques: Linear/Logistic Regression, Tree Based Models,
Matrix Factorization, Predictive/Classification Models, Clustering,
Learning to Rank, Deep Learning, Reinforcement Learning, Offline
Evaluation, A/B Testing, Named Entity Extraction, Feature
Engineering/Representation, among others.
Data Science Tools: Scikit, Vowpal Wabbit, Weka, Orange,
PySpark, Hadoop, XGBoost, LightGBM, CatBoost, TensorFlow,
Keras, Pandas, Numpy, Statsmodels, Caret, MLR, L2R,
DataFrames.jl, Plotly, Seaborn, Matplotlib, Spark Libaries, GLM,
Mocha, JuMP, Parallel, CSVKit, Stats, Hypothesis Tests, NLTK,
Scipy, Gensim, FastText, Scrapy and BeautifulSoup.
Data Science Env: Databricks, SageMaker, Jupter Lab, and EC2.
Programming Languages: Python, R, Julia, Java, Scala, Go, C#,
C++, C, Javascript, Prolog, Haskell, and Shell.
Softwares: VIM, PyCharm, Sublime, Juno, Eclipse, NetBeans,
Visual Studio, RStudio, Rational Software Architecture, MS Office,
Latex, VMBox, VMWare, Dev-C++, Git, GitHub, GiLab.
Web Technologies: Django, Flask, Play, GWT, Node.JS, JPA,
EJB, REST, xHTML, Markdown, PHP, .NET(VB.NET e C#), JSP.
Cloud Services: AWS and Google Cloud.
Databases: Solr, Elasticsearch, Redis/Dynamo, RDS, MongoDB,
SQL Server, Oracle, MySQL, and PostgreSQL.
Integration Technologies: Ansible, Docker, Vagrant, Packer, Chef
and CICD (Travis- CI, Jenkins and Buildkite), Datadog/NewRelic.
Paradigms: Object Oriented Programming and Functional
Programming.
Methodologies: Extreme Programming (XP), SCRUM, Agil
Methodologies in general, TDD, and BDD.
Platforms: Linux/UNIX, Windows and Mac OSX.
Idioms
English
Reading, Writing, Speech: Fluent.
Spanish
Reading, Writing, Speech: Basic.
Portuguese
Reading, Writing, Speech: Native.
Last time updated on 04 October 2020.