How Mentorship and Hands-On Learning Led to an Amazon Career

Gabriela Olinto ’16 chose RIT for a graduate experience that blended statistical theory with meaningful application. “I was immediately intrigued by the program’s reputation for hands-on learning.” The Applied Statistics MS program stood out not only for its practical focus but also for its faculty expertise. “Seeing that RIT had professors leading in machine learning, especially Professor Ernest Fokoué, was the deciding factor for me.”

RIT’s strong emphasis on career readiness and community also shaped Gabriela’s experience. “Professional development was built right into the program,” she said, recalling the resume workshops, interview preparation, and career fairs that boosted her confidence. Those resources helped her secure her first data scientist internship at Soleo Communications, where she worked with natural language algorithms and expanded her machine learning skills. Professor Jason Nordhaus, who consulted with the company, inspired her with his interdisciplinary approach, adapting Bayesian modeling from physical simulations to the challenges of search data.”

Gabriela now works as an Applied Science Manager at Amazon, where she leads research and science teams focused on fraud prevention. “My day-to-day is about guiding teams to build machine learning solutions that anticipate and counter threats,” helping ensure the platform remains safe for millions of users. She also oversees research initiatives, including recent work in Large Language Model optimization, a balance that reflects the applied mindset she developed at RIT. “I really enjoy bridging academic-level research with customer-centric applications.”

Faculty mentorship within the College of Science played a central role in developing Gabriela’s skills. Professor Joseph Voelkel’s lab classes reshaped how she approached data, a lesson that helped her build an industry-ready portfolio. “He showed me that data analysis isn’t just math, it’s storytelling.” Professor Ernest Fokoué provided the machine learning foundation she uses daily at Amazon and strengthened her communication skills through weekly paper debates in his data science research group. With his encouragement, she went beyond the requirements and published her thesis, gaining valuable exposure while presenting to scientists across Upstate New York. “That taught me how to translate complex ideas for any audience.”

For students pursuing statistics and data science, Gabriela encourages embracing opportunities that push you beyond your comfort zone. “Look for mentors who push you to publish and network. Don’t be afraid to take on challenges that stretch you. That’s where you grow the most.” In true Tiger spirit, she ends with a simple message: Go Tigers!