Experimentation
Designing A/B tests with clear hypotheses, appropriate metrics, and valid statistical comparisons.
Data science · Causal inference · Applied statistics
Daniel
Jordan Viray
Data scientist and analytics lead with 9 years of experience using quantitative methods to influence business decisions across executive and cross-functional teams. Strong foundation in analytics engineering, data visualization, and stakeholder communication, with growing experience in causal inference, machine learning, and deep learning. Focus on applications in healthcare.
01 / About
Data science for better decisions.
I use quantitative methods to evaluate impact and inform business decisions. My work includes framing research questions, designing experiments, estimating causal effects, building statistical and machine learning models, and communicating results with their assumptions and uncertainty. I’m increasingly interested in applying modern data science and machine learning to biology and the life sciences.
02 / Areas of focus
Methods and areas of focus.
Causal inference
Using experimental and observational methods to estimate the effects of programs, products, and interventions.
Applied statistics
Applying statistical methods to quantify uncertainty, test assumptions, and support evidence-based decisions.
Machine learning
Developing regression, classification, time-series, natural language processing, and deep learning models.
Analytics
Building ELT data pipelines, data visualizations, and self-service dashboards that produce actionable insights.
03 / Education
Quantitative and scientific training.
SCIENTIFIC TRAINING
Postbaccalaureate Certificate in Pre-Medical Sciences
Bryn Mawr College
A foundation in the biological and physical sciences.
QUANTITATIVE TRAINING
M.S. in Economics
Imperial College London Business School
B.A. in Economics
University of California, Los Angeles
04 / Connect
Contact
For professional inquiries related to data science, experimentation, causal inference, applied statistics, or machine learning.