This Data Neighbor podcast episode follows a senior data scientist at Microsoft from a small town in Nepal through a 2009 recession, US military enlistment and federal data work into big tech. The guest's argument is that the detour was an asset: the military built resilience and leadership that he says take longer to learn than technical skills, while years as a cyber-defense analyst gave him the domain knowledge and customer focus that, in his account, matter more than model building. He ends on advice to new graduates: a personal roadmap, a mentor, and one subject learned fully before the next.
Getting to the US. The guest stayed in Nepal for a computer science degree at the Nepal College of Information Technology, turning down admission abroad because the undergraduate scholarship at home was larger and international students faced costs after 9/11. He came to the US in 2007 for a master's in information systems in upstate New York.
The 2009 decision. Graduating into high unemployment, he joined a job consultancy in New Jersey and left after about a week because it applied his resume to every role regardless of fit. With his OPT due to expire within a year, a friend on the subway passed him a recruiter's card for a temporary government program recruiting people with specialties; he was 60% to 70% decided on the 90-minute train ride home. His family's first answer was to come home. He signed around October 2009, kept the 30-day option to cancel, and started training in February 2010.
Training. Basic combat training was three months, from 4am to 10pm, with no phone and no weekends, required of every recruit whatever their job. The hardest part for him was mental: standing beside high-school graduates with a master's degree and wondering whether he was going backward. Five months of job training followed in supply and logistics, a role chosen because non-citizens could not work on computer data.
Preparing the exit. He built a Microsoft Access database and forms to replace his unit's Excel files and took a military-funded online MBA. Facing the choice to re-enlist, he saved three months of terminal leave and moved to San Diego, where VA-approved courses carried a housing benefit. Within a month he had a contract data analyst role with a US Navy cyber security unit, went full-time within about six months, and later was selected for a federal program funding a data science master's at the University of San Diego.
Data science at Microsoft. He now works on the security side as something like an internal consultant, building data science for teams with or without their own. He argues ML features come from human experience of the domain and that AutoML without subject-matter experts may not work. His hardest problem type is anomaly detection: tuning for recall creates false positives that erode trust, and benign positives from red teams complicate labelling.