Laura Halacheva

Canadian Heritage Information Network

Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.

Experienced in data modeling, statistics, & machine learning.

Steven Ma

Google

2018 SABR Analytics: Diamond Dollars Case Competition

Influencer Marketing Platform for Brands and Advertisers

ASUS

Merck

At Google, Steven Ma works as a data analyst providing data analysis, data visualization and automation processes to improve efficiency and to support business decisions. He has strong knowledge of statistical models, machine learning models and quantitative fields and proficient in Excel, Python, SQL, R and Tableau. His experience also includes conducting marketing and business analytics and financial data analysis for companies across various industries such as ASUS, Merck and Cathay Real Estate.

Data and marketing analyst for Google & Merck.

Priya Laxman

Priya Laxman is a data professional with four years of industry experience specializing in forecasting, predictive modeling, data visualization, data modeling, digital analytics, and supply chain analytics. She was a digital data analyst at MYER, where she analyzed data to uncover insight used to improve the digital customer experience and drive business outcomes. At 460degrees, Priya worked as a data consultant for clients such as VicTrack, Telstra, and DBM Consultants. She is also proficient in tools like Tableau, Google Analytics, Data Studio, BigQuery, Hotjar, SQL, Python, R, Power BI, SAS, Python, Adobe Analytics, and Net Insight.

Former digital data analyst for MYER.

Pete Melgren

GitHub

Pete Melgren is a data scientist with seven years of experience working on end-to-end predictive analytics projects. He has consulted with cross-industry clients on the use of data science concepts such as machine learning, data mining, business intelligence, and predictive analytics to meet their business needs. As a former data scientist for the Cincinnati Reds, he performed in-depth research of advanced baseball physics measures using statistical techniques that include linear regression, linear classification, mixed models, GAM models, and machine learning methods such as XGBoost. Pete also has advanced knowledge of SQL, R, Python, and AWS.

Former data scientist for the Cincinnati Reds.

Peter Faasse

Afstudeerbegeleider

Nova Talent

WESP Impact Project

As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.

Former data science manager for Career Analytics.

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