Mission

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it's ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear.

In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It's a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

Role Purpose

Neko is opening clinics at pace: from a handful of US sites today to a 2028 cohort in the dozens and a portfolio in the hundreds by 2030. We are looking for a flexible, quantitative analyst to build the market intelligence behind that growth, using GIS, data, and analytics to decide where we open next, why, and how many clinics a market can support. The work runs from market sizing and member analytics through to site, market, and portfolio performance.

This is a build role. Neko is appointment-based, not footfall-driven, so the standard retail playbook does not transfer, and working out what predicts a successful clinic is the core problem. There is no framework to inherit: you will build the models and processes from scratch, and what you build in year one is what the whole portfolio runs on.

You will report to the Head of US Real Estate and work closely with the Transaction Managers, the Real Estate Associates, and our data analytics and engineering teams, in a small team where you help set the standard.

What You'll Deliver in the First 6-12 Months

• Market sizing: size the addressable member base by market and trade area, and translate it into how many clinics a market can support and in what order we should enter. This is the first priority of the role.

• Site selection support: give Real Estate the analysis to justify new markets and infill locations, and to decide clinic sizing and spacing, including overlap and cannibalization as density increases.

• Member data foundation: work with the internal data analytics and engineering teams to set up the full chain, from data collection to aggregation to analysis. Define our ideal member profile, find where those members live and work, and keep refining it as first-party data accumulates.

• GIS platform, tool agnostic: use GrowthFactor today and own the catchment definitions, data layers, and scoring logic. Document the logic independently of the tool so it moves with us, and help evaluate and onboard the next platform when the time comes. Make the tool usable by Transaction Managers, not only by you.

• Scoring and forecasting models: maintain a repeatable, defensible site scoring model and demand forecasts built to be re-run. A reforecast after a slipped opening or a shifted plan should be a scenario switch, not a rebuild.

• Portfolio, market, and site performance: as clinics open, track and review performance at the portfolio, market, and site level. Compare forecast to actual, separate a bad site from a bad market from a bad launch, surface underperformers early, and document what each opening teaches us. Turn those learnings into updated site selection criteria, scoring weights, and sizing and spacing rules, so every new clinic is chosen with better information than the last.

• Approval memos and cross-functional support: help produce LOI and Lease Approval Memos by coordinating inputs from Real Estate, Design, Construction, and Legal into a clear, one-page recommendation that leadership can decide from.

• Process design and redesign: own the evaluation processes the team runs on, and version them. Retire what stops working, rebuild what needs it, and write it down so others can run it without you.

• AI across the Real Estate team: use Claude and custom agents to eliminate your own manual work, then go further. Identify where AI can save broader team time (market research, data pulls, memo drafting, lease and LOI summaries, pipeline reporting), build reusable prompts, skills, and agents, train teammates to use them, and track the time saved. Be honest about where AI output needs human review.

• Market packs and benchmarks: produce market entry recommendations, ranked pipeline views, and site-level briefs, backed by per-site cost and occupancy benchmarks by market so deal comparison sits on real data.
  
  

Minimum Qualifications

• 3 to 6 years in market analytics, site selection, location intelligence, data science, or strategy, ideally supporting multi-site physical expansion.

• Hands-on GIS experience with GrowthFactor, Esri ArcGIS, Kalibrate, Buxton, SiteZeus, Placer, or similar. We care more that you understand catchment and scoring logic than that you know any one tool, because the tool will change.

• Strong market sizing and quantitative skills: penetration and capture modeling, regression or other predictive approaches, and scenario and sensitivity analysis. Excel to a high standard.

• Comfort with census, consumer spend, mobility, and first-party data, and honesty about the limits of each.

• Financial literacy: you can build a basic site pro forma, calculate payback and break-even, and connect a demand forecast to a real estate decision.

• Flexibility. You can tear down your own model without ego, rebuild it when the facts change, and stay organized while priorities shift.

• Clear communication and intellectual honesty. You can defend an assumption to a skeptical executive in two minutes, name the two things most likely to be wrong, and say early when the data does not support the answer people want.

Preferred Qualifications

• SQL or Python, which helps you work alongside engineers.

• Specific, demonstrable use of AI to speed up analytical work, and real interest in teaching others to do the same.

• Exposure to healthcare or regulated markets, or to European or multi-region expansion.