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.

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.

Pawan Saxena is a data scientist working at Tiger Analytics, where he applies different data science and machine learning techniques on a data science project. He is highly skilled in R programming, Python, Java, machine learning, deep learning, Flask, MySQL, TensorFlow, Keras, Pandas, Numpy, Tableau, PySpark, AWS, Sagemaker, and Azure ML Studio. Previously worked as a technical content engineer, he wrote and implemented more than 100 articles on various topics related to data analysis, data visualization, machine learning, deep learning, and NLP articles such as Named Entity Recognition, BERT, Rasa, GPT-3, and more.

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.

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.

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.

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.