Data scientist CV Sample

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Steven Brandon

Data scientist – Data Engineer

Summary

A highly competent data scientist with seven years of experience developing a wide range of models like Credit Scoring, Predictive Segmentation & RFM, and Customer 360 etc. Ability to use (data) statistics and machine learning for finding complex data patterns that drive meaningful impact on the business. 

I am looking for the opportunity to build a challenging career and apply my skills in an innovative and simplify  process. I enjoy working in a team and communicating data-driven results.

Skills

  • Python
  • Spark
  • SQL
  • PowerBI & Tableau

Work Experience

Data Scientist

NT Group – New York

Feb 2021

  • Predictive RFM model (Nepal-Ncell): predictive segmentation model based on 12 Million subscriber’s historical behaviour (recency, frequency and monetary), used in campaign management.
  • Gross Adds Optimization (Bangladesh-Robi): Contributed to the project by building timeseries models to predict Add back’s ARPU and subscriber count at state level.
  • Next Best Action – NBA (Celcom-Malaysia): Contributed in development of Celcom NBA which helps personalize the offers using subscriber’s historical purchase data.
  • Data management and pipelines (Malaysia-Apigate): Synchronizing more that 1T of data between AWS and GCP datalake, design and implement more than 80 daily, hourly and near real-time pipelines.
  • Stretch model (Malaysia-Apigate): Help business team to identify segment of customer that can be targeted as recoverable revenue by processing up to 1 billion records of transactional data.
  • Customer journey (Malaysia-Apigate): end to end segmentation model of transactions that help business team apply data driven decisions on products and content providers.

Big Data Analyst

Delta Solution

Aug 2019

  • Credit Scoring (Sri Lanka-Dialog): Developed different models for fintech microlending and cross selling, based on telco subscriber’s products usage and transactional behaviour and retail revenue and transactional history.
  • DRIP – Predictive Customer Segmentation (Nepal-Ncell): Developed predictive models for customer ARPU and data usage segmentations based on 10 million customer’s historical data. Output of predictive model is used in campaign management which produced incremental revenue.

Senior Engineer

Mark Tech

Sep 2018

  • As team member to perform research and development in field of cloud computing and edge computing which led to creation of a platform called Mi-Focus. This platform was used by ministry of Health to deploy Teleprimary Care – Oral Health Clinical Information System (TPC-OHCIS) at clinics in Malaysia.

Education

Master of Computer Science

San Jose State University

Sep 2016


Bachelor of Computer Science & Networking

Northeastern University

Sep 2012


Project

Stock Price Prediction

JP Morgan Asset Management – Florida

Sep 2016

Use the Sentiment Analysis Algorithms to understand the stock sentiments. 

Project: Twitter Sentiment Analysis 

Tools: NLTK, Python 

Algorithms: Sentiment Analysis

Stock Recommender Systems

JP Morgan Asset Management – Florida

Aug 2015

Recommender systems have become the most popular feature of the stock market. We have developed a artificial intelegint stock recommender tool using the Deep Neural Networks and classification algorithms 

Tools: Python, sklearn

Algorithms: Deep Neural Networks, classification algorithms

Customer Support System

Data Scientist

May 2016

Customer support has become a challenging job for every business. We build a “customer support system” to address and support the needs of the customers. 

Tools: OpenCV, Python 

Algorithms: Convolution Neural Network and other facial detection algorithms

Languages

  • English
  • French
  • Arabic
  • German

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