Data Science Consultant at Slooh

Since June 2026, Alisa has worked as a Data Science Consultant at Slooh, a space education and research company that operates a global network of telescopes for schools, universities, and individual members. Working in SQL, Python, and Looker against the company’s warehouse, she built Slooh’s live analytics layer, replacing repetitive data pulls with a canonical set of definitions covering how subscription tiers map, and how cohorts are constructed. These definitions now underpin reporting across both the consumer and institutional sides of the business. On this foundation she developed health-score models covering over 15,000 users, translating raw subscription and engagement signals into a structured view of retention that leadership now uses to prioritize outreach and renewal conversations. 

Her latest project extends that work by building a “second brain” for the team, a self-updating knowledge base in Claude and Obsidian modelled on Andrej Karpathy’s LLM-maintained wiki. It captures institutional context, metric definitions, open questions, prior analyses, and the reasoning behind past decisions. The knowledge base keeps analyses reproducible, and any number presented on the live reports can be traced back to the query and assumptions that produced it. 

She entered the role with the following learning goals. The first was to find out whether she could perform as a decision partner rather than a request taker by framing questions, choosing an approach, and bringing recommendations directly to leadership. The second was to work with data in its native form, rather than with data prepared or cleaned in advance for a specific task. The most demanding problems were rarely the modeling ones. They were definitional. Establishing precisely what each question was asking, what counts as an active customer, which engagement signals indicate value, and when a subscription is actually at risk, proved more consequential than any technique applied afterward. She learned that warehouse data is a record of how a business operates, and that reading it well depends less on statistical methods than on understanding how a company bills, how its members engage, and how subscriptions renew. That distinction clarified her direction for future roles. She is drawn to work that requires both technical depth and business judgement. 

She is very grateful to the Emerson Foundation - Data Science Fund for the financial support that made this opportunity possible. What made it meaningful was not only the technical work but the mission behind it. Slooh’s premise is that space is for everyone. Owning a telescope has never been realistic for most people or most schools, so Slooh built a global network of observatories that anyone can control from their laptop, capturing and analyzing their own images of the night sky. Working on the data behind that mission meant users in her models were never abstractions, they were people encountering something genuinely inspiring. She is thankful to have spent this time building something lasting for an organization that is changing the future of space education.