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Bruno Luiz Mendes



D A T A   a n d   TECHNOLOGIES

About Me

I am a peaceful person, determined to achieve my goals, always taking advantage of the journey to this and the relationships built, I really like to solve complex things, maybe it's because I've heard a few times in my life that I wouldn't be able to, so it motivates me sometimes.

  • Age 25
  • Residence Brazil
  • Office Address Anywhere
  • e-mail mendesbruno@outlook.com
  • Phone +55 11 94129 3048

What I Do

Data Science

Data science is not a seven-headed animal, but it is also no walk in the park, studying and working with this science is exciting, as it makes me keep my head in constant study.

Data Analytics

Being a data scientists is essentially having a job as a detective, the modern day Sherlock Holmes. The industry is great for those with curious minds who love solving everyday puzzles. Working with data allows you to play with knowledge.

Innovation

Working innovating means working freely, where there are no barriers to development.

Management

The art of managing people is still new to me, however, I like it and I always try to improve myself a little more.

Fun Facts

Happy Clients

08

Working Hours

15841

Awards Won

02

Resume

Education

2023 (studyng)
Massachusetts Institute of Technology

Data Science and Statistics

Online Master of data science, statistics, and machine learning

2020 (completed)
Universidade São Judas Tadeu

Bachelor of Computer Engineering

The focus is to prepare the student to produce new machines and computer equipment used in different sectors, according to the needs of the market, in addition to planning, implementing computer networks and their components, and acting in the development of software.

Experience

2021 - Current
Zapay

Data Leader

Responsible for leading and developing the data team, in addition, developing strategies for data monetization, either with algorithms or micro products.

2020 - 2021
Cia Hering

Senior Data Scientist

⦿ Identifying relevant data sources for business needs.

⦿ Collecting structured and unstructured data.

⦿ Organising data in to usable formats.

⦿ Building predictive, classificatory and descriptive models.

⦿ Building machine learning algorithms.

2020 - 2020
Stoodi

Senior Data Analytics

⦿ Responsible for starting the DBM and Customer Analyzes area, structuring crm processes;

⦿ Develop analytical studies using data analysis in python;

⦿ Lead scoring model development with python and sql programming.

⦿ Implementation of salesforce's cloud marketing.

2017 - 2020
Marketdata

Data Analyst

I was responsible for articulating all the data load management and maintenance of elt systems, in addition, customer segmentation for marketing campaigns as part of my daily life.

Soft Skills

Emotional Intelligence

75%

Communication

85%

Decision Making

90%

Time Management

85%

Coding Skills

Python

95%

SQL

95%

Data Skills

Machine Learning

95%

Data Vizualization

90%

Data Wrangling | ETL | Pipelines

90%

AWS Skills

RDS, DynamoDB, Athena

95%

Glue and DMS

80%

SageMaker and Lambda

80%

Knowledges

  • Machine Learning
  • Deep Learning
  • Artificial intelligence
  • Data Munging
  • Time Management
  • Communication
  • Problem-Solving
  • Social Networking
  • Flexibility
  • Big Data
  • Mathematics
  • SaaS
  • Unstructured Data
  • Statistics
  • Business
  • Google Analytics
  • Cloud

Certificates

Psyhology of Intertnation Design

Membership ID: XXXX
19 April 2018

Psyhology of Intertnation Design

Membership ID: XXXX
19 April 2018

Portfolio

Churn

Churn

Articles
Media Project 2

Make Data Science Easy [practice] Vehicle Insurance EDA

Detailed
Media Project 1

Salesforce Marketing: Get Started with Python SDK

Detailed
Media Project 1

Salesforce Marketing Cloud — Easy Data Integration — part 1

Detailed
Media Project 1

Segmente melhor seus clientes utilizando uma simples análise exploratória em python.

Detailed
Media Project 1

O que é Database Marketing e como se beneficiar dessa estratégia ?

Detailed
Media Project 1

Make Data Science easier: Evaluating Analyses — Introduction

Detailed
Media Project 1

Making a data science easier: Noise vs Outliers

Detailed