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
🎙️ In episode 2 of "AI and ML Conversations," I sit down with Sebastián Poliak, an experienced machine learning engineer who's transitioned into an independent app developer. 🚀
Sebastián opens up about his path from applied research to building AI-powered mobile apps like Stridly and Babli (https://publicspeakingcoach.app/).
With notable roles including Senior ML Engineer at Bloomreach and Machine Learning Researcher at Seznam, he brings a wealth of expertise.
We explore the changing world of machine learning engineers, the influence of generative AI, and why focusing on high-quality products with great user experiences matters.
Join us for practical tips on career shifts, integrating AI into app development, and making the most of data-driven strategies - the links are in the comments.
Timestamps:
00:00 – Introduction
01:22 – Sebastián’s background & early interest in AI
03:42 – Career as a machine learning engineer
08:02 – Key projects: search, NLP & recommender systems
11:33 – The impact of transformers & GenAI
14:19 – From ML engineer to indie app developer
24:03 – Building and launching first apps
26:50 – Marketing strategies
30:13 – Data-driven product design & user experience
37:55 – Using AI in development & backend setup
40:19 – Costs, fine-tuning, and evaluation challenges
48:22 – Focus on onboarding, growth & staying solo
52:32 – Influencer marketing & growth hacks
55:15 – Overview of Sebastián’s apps
57:54 – Life advice: freedom, happiness, and building quality products
01:00:23 – Closing
In this conversation, Dimitar Nentchev shares his journey from aspiring professional soccer player to a successful career in data science and analytics. He discusses the challenges he faced during his transition, the importance of aligning data projects with business needs, and the impact of AI and LLMs on productivity. Dimitar emphasizes the value of iterative development and the necessity of quality data in machine learning projects, while also reflecting on his experiences in both startups and large organizations. The conversation also delves into personal experiences with mental health, work-life balance, and the significance of taking risks in one's career.
