Topic
Data Analytics
All Topics
A/B Testing (10) API (1) Artificial Intelligence (36) Automation (6) Automattic (23) Best Practices (12) Bias in AI (6) Books and Reading (6) Career and Professional Development (15) Causal Inference (1) Communication (15) Conferences and Events (5) Data (1) Data Analytics (26) Data Discoverability (4) Data Engineering (12) Data Ethics (10) Data Lineage (1) Data Products (1) Data Science (56) Data Speaker Series (4) Data Visualization (24) Deep Learning (3) Distributed Work (8) Diversity and Inclusion (8) Ecommerce (1) Elasticsearch (8) Experimentation (13) Information Retrieval (6) Internet (1) Learning and Education (6) Machine Learning (43) Marketing Analytics (8) Meetup (3) Metadata (1) Natural Language Processing (6) Network Analysis (8) OpenMetadata (1) Open Source (7) Productivity (11) Python (8) Remote Work (21) Scientific Communication (10) Semantic Search (2) Social Media Analytics (1) Software Engineering (4) Surveys and Research Methods (1) Tech Industry (14) Technology Trends (1) Time Series Analysis (3) Transparency in Data (2) WooCommerce (1) WordPress (23) WordPress.com (2) Work Life Balance (6)
-
Predicting the past: how big was the internet?
Ordinarily, data scientists spend a lot of time trying to predict the future. Time‑series prediction, recommendations, even next‑token‑prediction…they’ve all broadly got the same flavor. Recently, however, I came across a problem with a distinctly different taste, and I’d like to share it with you today: predicting the past. Surely predicting the past is pretty easy, right?…
-
Move Fast and Don’t Break Things: Shipping the Simplenote MCP
When Automattic recently launched a month‑long hackathon, engineers Mark Biek and Evan Tobiesen knew exactly what they wanted to work on: the Simplenote Model Context Protocol (MCP) server. Neither Mark nor Evan works in data science, so measurement might have been the easy thing to skip. Instead, they shipped a product with built‑in measurement from…
-
From Messages to Episodes: Building Better Recall for AI Support
Our customers do not think of support as isolated chats. They think in terms of things they are trying to get done: connecting a domain, changing the look of a site, launching a store, fixing checkout, recovering access, or understanding a bill. That creates a challenge for an AI support assistant. If a customer returns and says: Let’s…
-
From Support to Data Science and Analytics: My Journey at Automattic
“Is it possible to transform a role in customer support into a data science career?” This question, which once seemed like a distant dream, became my career blueprint at Automattic. This post describes the journey to a data wrangler in a tale of continuous evolution, learning, and adaptation.
-
Hack Project: Creating a tool to translate customer feedback into product insights
Explore Automattic’s hack project in our latest post, where we developed a tool for analyzing Tumblr user feedback. Learn about our challenges with AI, creating insightful dashboards, and the impactful outcomes. Our collaborative effort not only enhanced product development but also earned us the top spot in our Hack Week Awards!
