About Me
Hello! My name is Evan Velasco. I am a digital analytics professional currently working as a Senior Data Scientist at Instacart.
I began my career focused on programmatic data and understanding the complex landscape of ad tech. Since then, I have continued to
gain knowledge of digital advertising as a whole. I am an extremely driven individual with a natural curiosity who is always
eager to learn something new.
My Day Job
I am currently a Senior Data Scientist at Instacart, where I lead all major off‑platform analytical initiatives.
My favorite projects focus on audience segmentation and targeting R&D. I have developed more than 20 methodologies for
unique audience types and filtering strategies. I am fortunate to work with a talented team of engineers who
have built the infrastructure that enables me to deploy these methods into a self‑serve custom audience builder.
Education
I am a current student at Georgia Tech pursuing a Masters of Science in Analytics. My decision to go back to school
was a natural progression as I continued to seek knowledge. I chose the program at Georgia Tech due to its academic
rigor and wide array of course choices. I am most interested in learning about the mathematics behind
modeling algorithms. A few courses I am looking forward to include Optimization, Regression, and High Dimensional Data Analytics.
I am a proud graduate of UC San Diego where I received a B.S. in Joint Mathematics-Economics.
The Math-Econ major is very broad and allows for flexibility depending on an individual's
interests. I chose to focus my coursework on statistics which introduced me to the field of analytics and data science.
I had the pleasure of taking multiple computational statistics courses based in R and data science courses in Python. I paired my applied statistical courses with rigorous mathematical theory, including multiple courses in real analysis.
My degree formed a strong foundation which I continue to build upon through self studying and professional experience.
Interests
Currently Studying:
Deep Learning:
This course covers the fundamental principles, underlying mathematics, and implementation details of neural
networks. Topics range from core building blocks like backpropagation, convolution layers, and activation functions to complete architectures
such as CNNs and RNNs. Applications span computer vision, natural language processing, and reinforcement learning.
Industry Topics:
Within the ads industry I have a wide array of interests ranging from programmatic auction mechanisms to first party data solutions. In 2026 I am most excited to learn more about leveraging data cleanrooms for measurement and data collaboration in ad tech.