- Lowongan
- Panduan KarirPengembangan KarirWawancara Orang/PerusahaanBusiness ExcellenceDari CakeResumeBacaan Unggulan
- Untuk Perusahaan
- Download aplikasi CakeResume
CakeResume Talent Search
Paket Perekrutan Paling Mudah dan Efektif, Pilihan Ratusan Perusahaan
Cari lebih dari 800 ribu CV dan ambil aksi menghubungi pelamar kerja untuk rekrutmen yang lebih efektif. Pilihan ratusan perusahaan.
- Lihat semua hasil pencarian
- Tanpa batas harian untuk memulai pesan baru
- CV dapat diakses oleh perusahaan berbayar
- Lihat email pengguna & nomor telepon
Definition of Reputation Credits
Rafael Meirelles
I have experience in different segments of industry, such as strategic
consulting, financial, telecommunications and technology. All these
experiences helped me to improve my statistical and analytical
Data Scientist
Sao Paulo, BR
[email protected]
Work Experience
Wildlife Studios, Data Scientist, May 2020 ~ Present
Development and maintenance of machine learning models to predict LTV and support the user acquisition
Tools: Python, R, Spark
Nextel Brasil, Data Scientist, Jan 2018 ~ May 2020
I worked on the Deep Analytics team identifying and acting on opportunities to apply machine learning on different areas of the company to increase revenue and make processes more efficient.
- Initiatives examples :
- NPS modeling : understand how the network variables affect the NPS score of the company and use them to predict the detractors.
- Click Attribution : Comprehend the customer journey step-by-step using a
Markov Chain approach
-Court Decisions : Predict the probability of a lost cause as well as the amount of the sentence involved in the legal case so Nextel can attempt to sign agreements when it makes sense
- Network degradation study : Comprehend the concepts of KQIs such as RTT,
Lost Packages and Retransmission rate. The study identified spots where these indicators needed to improve, ploting all of them in maps KML and using them as features to explain events like churn and complains using
XGboost and Shapley Value
- Tools :
- Python/R as statistical tools using libraries like pandas, matplotlib, scikit, dplyr, ggplot and caret
- XGBoost, Random Forest, Shapley Value, Logistic Regression, Time Series as algorithms for statistical modeling
- SQL Impala/Hive in a data lake environment to data extraction
Movile, Data Scientist, Jul 2016 ~ Feb 2018
I worked on the Machine Learning Team developing statistical models and data analysis to support decisions of several internal clients on Movile
I used tools like R, Python, Microsoft Excel and SQL to extract as much value as possible from data and improve results.
A few examples of Data Science projects that I worked are : Client
Segmentation RFM analysis, Churn probability Survival Analysis and ETA for delivery services Linear regression, XGboost, Deep Learning and KNN.
Tools: SQL, Python and R
Telefonica, Data Scientist, Mar 2016 ~ Jul 2016
Development of classification models Logistic regression, Decision tree and
Random Forest oriented for churn.
Tools: SAS, SQL and R
TIM Brasil, Data Scientist, Jul 2015 ~ Mar 2016
Development of models focused on understand behaviour based on
geolocation and sociodemographic variables. The main focus was in cluster analysis to predict buying propensity in home broadband.
Tools : SAS, R and Microsoft Excel
Value Partners, Intern, Sep 2013 ~ Dec 2013
I worked as an intern providing support to the consultants in projects in different segments such as services, oil and gas and chemicals.
Education
Massachusetts Institute of Technology, Masters Statistics and Data Science, 2020 ~ 2021
This MicroMasters program in Statistics and Data Science was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners around the world. It focuses on big data and making data-driven predictions through probabilistic modeling and statistical inference; identifying and deploying appropriate modeling and methodologies in order to extract meaningful information for decision making
Udacity, NanoDegree Program , Data Science and Business, 2017 ~ 2018
Nanodegree focused in understanding several machine learning techniques and applying them to a rich set of business problems.
Universidade Estadual de Campinas, Statistics, 2010 ~ 2013
-Graduated in 2013, in 4 years, at Unicamp as the 8th top student (total of 74)
-Coursed several disciplines to improve my skills in modeling and to better understand the applications of statistics in economics: Econometrics, econophysics, stochastic process, demographics
-Developed a scientific research in statistical quality for a year ( 2011 - 2012) with a PIBIC scholarship: "Monitoramento e controle de atributos de pequena probabilidade de ocorrência usando as estatísticas p e g : uma análise de performance"
Rafael Meirelles
I have experience in different segments of industry, such as strategic
consulting, financial, telecommunications and technology. All these
experiences helped me to improve my statistical and analytical
Data Scientist
Sao Paulo, BR
[email protected]
Work Experience
Wildlife Studios, Data Scientist, May 2020 ~ Present
Development and maintenance of machine learning models to predict LTV and support the user acquisition
Tools: Python, R, Spark
Nextel Brasil, Data Scientist, Jan 2018 ~ May 2020
I worked on the Deep Analytics team identifying and acting on opportunities to apply machine learning on different areas of the company to increase revenue and make processes more efficient.
- Initiatives examples :
- NPS modeling : understand how the network variables affect the NPS score of the company and use them to predict the detractors.
- Click Attribution : Comprehend the customer journey step-by-step using a
Markov Chain approach
-Court Decisions : Predict the probability of a lost cause as well as the amount of the sentence involved in the legal case so Nextel can attempt to sign agreements when it makes sense
- Network degradation study : Comprehend the concepts of KQIs such as RTT,
Lost Packages and Retransmission rate. The study identified spots where these indicators needed to improve, ploting all of them in maps KML and using them as features to explain events like churn and complains using
XGboost and Shapley Value
- Tools :
- Python/R as statistical tools using libraries like pandas, matplotlib, scikit, dplyr, ggplot and caret
- XGBoost, Random Forest, Shapley Value, Logistic Regression, Time Series as algorithms for statistical modeling
- SQL Impala/Hive in a data lake environment to data extraction
Movile, Data Scientist, Jul 2016 ~ Feb 2018
I worked on the Machine Learning Team developing statistical models and data analysis to support decisions of several internal clients on Movile
I used tools like R, Python, Microsoft Excel and SQL to extract as much value as possible from data and improve results.
A few examples of Data Science projects that I worked are : Client
Segmentation RFM analysis, Churn probability Survival Analysis and ETA for delivery services Linear regression, XGboost, Deep Learning and KNN.
Tools: SQL, Python and R
Telefonica, Data Scientist, Mar 2016 ~ Jul 2016
Development of classification models Logistic regression, Decision tree and
Random Forest oriented for churn.
Tools: SAS, SQL and R
TIM Brasil, Data Scientist, Jul 2015 ~ Mar 2016
Development of models focused on understand behaviour based on
geolocation and sociodemographic variables. The main focus was in cluster analysis to predict buying propensity in home broadband.
Tools : SAS, R and Microsoft Excel
Value Partners, Intern, Sep 2013 ~ Dec 2013
I worked as an intern providing support to the consultants in projects in different segments such as services, oil and gas and chemicals.
Education
Massachusetts Institute of Technology, Masters Statistics and Data Science, 2020 ~ 2021
This MicroMasters program in Statistics and Data Science was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners around the world. It focuses on big data and making data-driven predictions through probabilistic modeling and statistical inference; identifying and deploying appropriate modeling and methodologies in order to extract meaningful information for decision making
Udacity, NanoDegree Program , Data Science and Business, 2017 ~ 2018
Nanodegree focused in understanding several machine learning techniques and applying them to a rich set of business problems.
Universidade Estadual de Campinas, Statistics, 2010 ~ 2013
-Graduated in 2013, in 4 years, at Unicamp as the 8th top student (total of 74)
-Coursed several disciplines to improve my skills in modeling and to better understand the applications of statistics in economics: Econometrics, econophysics, stochastic process, demographics
-Developed a scientific research in statistical quality for a year ( 2011 - 2012) with a PIBIC scholarship: "Monitoramento e controle de atributos de pequena probabilidade de ocorrência usando as estatísticas p e g : uma análise de performance"