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.

Yogini Naiki is a data scientist with over two years of experience in providing data-driven solutions for products and three years as a developer. She has worked on NLP, data engineering, descriptive analytics, and data visualization. As a data and applied scientist at Microsoft, she has handled multiple data science and analysis projects, including formulating OKRs and creating metrics and insights. Yogini also served as an analyst for Deutsche Bank. Her technical skills include Python, Java, R, Big Data systems (Cosmos + Scope, Hadoop), Pyspark+Databricks, Power BI, machine learning, and basic SQL.

Didi Adisaputro is a data scientist with over a decade of experience in data analytics, R development, data management, engineering, machine learning, big data, and data intelligence. He has profound experience in automation, building R Shiny dashboard, and data science pipeline build. As a climate intelligence section head at Golden Agri-Resources (GAR), he performs exploratory data analysis using R/Python and presents insights to business stakeholders to drive strategic decisions. His technical skills also include Keras and TensorFlow.

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.