Craig Sakuma is an experienced Data Analytics professional and has been providing corporate training about it. He has designed multiple curricula to effectively share his knowledge with clients such as Twitter, Microsoft, Intuit, Atlassian, Box, and Bloomberg. His clients also received training on prominent tools in terms of data analytics such as Python, SQL, Pandas, Scikit Learn, Tableau, Domo, Qlik, Plotly, and Matplotlib.

Experience leading the machine learning competency, which involves scoping machine learning problems and driving project adoption, deploying and managing fraud detection and NLP models in our production machine learning infrastructure on AWS, and building a new infrastructure for production machine learning using Spark and MLflow.

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.

Hung is a full-stack data scientist with expertise in both math statistics and agile software development. She has processed financial valuations of multi-million dollars for agricultural corporates, analyzed clinical data for major hospitals in Australia, and developed machine learning models for the EuroLeague - Europe’s premier basketball competition. She brings a wealth of international experience to her teams, having worked and studied in Singapore, France, Germany, Switzerland, and Australia. She completed her Ph.D. at UNSW Sydney with a Dean’s Award Nomination for the Top 10% of Theses.

Biochemist and data analyst with 2+ years of experience in data analyisis, data visualization and machine learning. Currently specialized in health related work as an epidemiologist.

Stephanie Mak is a highly experienced data and machine learning engineer with a strong track record of delivering data/ML projects, and enabling data-driven decision-making that aligns with business goals. She has also attained the DataBricks Certified Machine Learning Associate and DataBricks Certified Data Engineer Associate Certificates. Her latest work EnergyAustralia includes the design and delivery of DataBricks multi-workspace solutions for enterprise data lakehouse, data science laboratory, and ML engineering.

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.