Data scientist, customer segmentation and persona analysis (clustering, Python)

Data scientist, customer segmentation and persona analysis (clustering, Python) We're a subscription-based B2B service. We want a data scientist to help us understand who our customers are and how they behave, using two datasets we'll provide. Phase 1: we have a customer file enriched with third-party demographic data (household income, age, business type, and similar fields). Find the natural groupings in it. Clustering, dimensionality reduction, whatever method fits. The goal is a small number of coherent, well-differentiated personas. Phase 2: separately, we have in-app usage data. Feature adoption, session frequency, engagement patterns. Same exercise. Find the behavioral segments. Phase 3: bring the two together. Do the demographic personas and behavioral segments overlap? Does a given demographic profile predict a given usage pattern? We want one synthesis and a hypothesis around personas we will then validate with using surveys and interviews. What we're looking for: 1. Strong Python (pandas, scikit-learn) and SQL 2. Experience turning clustering output into personas a business team can actually use. Please share an example, including which algorithm you used and how you checked the clusters held up beyond statistical separation 3. Comfort engineering features across mixed categorical and continuous data 4. Ability to present findings in a short deck or written summary a non-technical reader can follow Scope: 40 to 60 hours across the three phases, structured as milestones. We'll start with Phase 1 as a paid trial and extend to Phases 2 and 3 based on fit. In your proposal, include a brief example of a past segmentation project: the algorithm, the data, and how you validated the clusters.