In episode 3 of "AI and ML Conversations," I sit down with Diogo, a senior data scientist at Usercentrics and a PhD researcher in data science, to unpack pragmatic data science, marketing measurement, and using LLMs with strong privacy guardrails.
Diogo traces his path from management and marketing into industry roles across Europe, balancing a remote career in Norway with research on measuring cultural value - drawing sharp parallels to brand equity, data scarcity, and business value.
We cover what it takes to be effective with quick proofs of concept, financial value proxies, and privacy-first use of LLMs for customer data enrichment.
The conversation also dives into remote vs office culture across countries, startup realities where roles blur across data and engineering, and lightweight rituals like bi‑weekly project reviews that keep stakeholders aligned and accountable.
Timestamps
00:00 - Introduction
00:40 - Guest intro: Diogo, background, Usercentrics
01:13 - Why a PhD and timing trade‑offs
05:02 - Cultural economics: measuring cultural value vs brand equity
07:41 - Data scarcity and useful variables: ticketing API, weather/holidays, telco footfall, surveys
09:19 - Economic impact: spillovers to housing and tourism; online reviews sentiment
11:59 - Moving from Portugal to Norway; EOR setup and distributed teams
13:15 - Remote vs office: flexibility, productivity, and policy pitfalls
16:55 - Portugal’s remote reality, expats, and housing pressure
19:04 - Ship value fast: POCs, value rules, pragmatic LTV signals
23:49 - Communicating with non‑technical stakeholders and focusing on business metrics
27:18 - Startup roles: DS, DE, MLE, AI eng; wearing multiple hats
30:34 - Meetings and ceremonies: beyond daily standups to bi‑weekly project cadences
34:57 - Toolbox: VS Code, schemas, and data discoverability pains
36:59 - The measurement trifecta: attribution, geo‑incrementality, and Marketing Mix Modelling (MMM)
39:35 - Adding external signals (e.g., Apple keynotes) to MMM
40:29 - LLMs for customer data enrichment and segmentation
42:26 - Hosting models on Vertex AI/Azure and privacy considerations
43:09 - Career advice: build close stakeholder relationships and iterate visibly
44:56 - Closing