Data scientist

AB-InBev is considered one of the largest fast-moving consumer goods companies in the world. I have done a variety of activities, such as:
Gather experience in several areas within the company, ranging from brewery process to sales;
Work on ad-hoc analysis from many databases and presented key insights to stakeholders;
Co-found data science study group where data scientists share best practises and conduct meetings presenting several techniques and tools;
Build statistical models using python, cloud computing and open-source libraries;
Translate the results of an analysis into easily understood presentation materials to several stakeholders, including global team;
Recency, Frequency, Monetary (RFM) and Customer Life Time Value (CLTV) analysis and prediction using Python and SQL for Customer Experience insights;
Predictive modelling to estimate, in days, next customer's purchase, allowing to understand their purchasing habits;
Client's health-score estimation used to measure the relationship with the customer, allowing to identify pain points

I joined IBM in 2017, being approved in the 'Consulting by Degrees' trainee program. As soon as I started, I was part of the advanced analytics team, and worked as a data scientist in several projects involving machine learning, optimisation, and also helping companies to be successful in their digital transformation. My activities involved identifying opportunities within the business area that could be solved by machine-learning.
Create reports using BI tools such as Power BI and Tableau to show relevant information to the clients and help in decision making.
Implement data mining projects using CRISP-DM and other methodologies, building statistical models applied to industry.
Contact with the following industry sectors: mining, consumer goods, retail, telecommunication, culture and entertainment, banking and education, understanding their products and services.
Design and implement proof of concept (POC) to demonstrate the capabilities to clients.
Mentoring colleagues in data science. During my career I was promoted twice in less than a year achieving excellent performance results