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Data Science
9 posts about Data Science. Every summary links to the original.
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Building Reliable Data Analytics Agents: Lessons from the KDD Cup
The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition by building a reliable agent system around a small, fixed LLM. The system constrained the agent’s action space through a harness that normalized data access by converting CSV and JSON files into SQLite tables, exposed a limited set of custom functions for schema inspection, SQL queries, and answer writing, and seeded the agent with schema context before analysis. Documents and videos were handled through targeted tools like prose_helper for extracting rules from documents and keyframe extraction with aligned transcripts for video evidence, keeping the main LLM context focused. The team emphasized inspectability by recording execution traces and running repeated attempts, using evaluation and trajectory review to identify failures and refine the harness. The key lesson is that reliability for data analytics agents often comes from the harness design, not the model’s capabilities.
Clear career paths for high-performing Data Science and Research teams
Intercom created a unified RAD function 18 months ago, merging Product Research and Product Analytics to drive evidence-based decisions. To build a high-performing team, they developed career ladders with two paths: individual contributor and manager, where management is a lateral move, not a promotion. The IC track spans Associate to Senior Principal and assesses six skill sets: mastery, partnerships and influence, impact, leadership, communication, and feedback and collaboration, with specific competencies like tools and methods, product knowledge, and mentorship. The management track focuses on five skill sets: leadership, people management, business strategy, product strategy, and execution and impact, where managers are expected to be role models, uplevel the RAD function, and coach their teams. These ladders, created with managers Nick, Louis, and Hayley, clarify roles, align expectations, promote development, and enable fair performance calibration, prioritizing mastery and communication across both paths.
Beyond the Model: Engineering AI Infra with Scientific Judgement
Airbnb describes Insight Miner, an agent harness designed to make unstructured data investigations scalable, reproducible, and audit-ready. Its problem was a bespoke process spanning data discovery, expert review, representative datasets, and manually curated methods that could not keep pace with new languages, geographies, and LLM-based products. The shared package combines extraction, embedding, and clustering with prompt tuning, hard-example mining, contrastive labeling, and a path from unsupervised exploration to classification, while an agent handles research questions, execution, and methodological guidance. Airbnb says investigations that once took months can now take days, allowing mechanical work to scale while data scientists spend more time inspecting ambiguous evidence, testing hypotheses, and improving techniques. The tool also lets nontechnical experts run analyses across use cases including survey coding, model evaluation, and fraud patterns.
Why Spotify Is Not Using Bayesian A/B Testing
Spotify argues that Bayesian A/B testing is not a single method but a family of configurations defined by a prior, likelihood, and stopping rule. It contrasts flat-prior posterior-probability stopping, which can reproduce frequentist peeking false-positive rates, with Bayes-factor stopping, which controls false positives under optional stopping, and calibrated empirical Bayes priors, which can shrink estimates and control false discovery rates. The post frames experiment design around program goals such as limiting harmful or ineffective launches, achieving estimation precision, and minimizing costs rather than selecting a statistical framework first. It says Bayesian and frequentist approaches overlap substantially, while Bayesian advantages such as richer probability statements require additional prior complexity. Spotify therefore continues using frequentist tooling because supporting two modes would add planning, monitoring, interpretation, and configuration costs without a sufficient benefit.
Introducing the Anthropic Economic Index
Anthropic introduced the Anthropic Economic Index to track how AI is used in work and to study its effects on labor markets over time. Its first report analyzes approximately one million anonymized Free and Pro Claude.ai conversations, using Clio to match them with occupational tasks from the U.S. Department of Labor’s O*NET database. Usage concentrated in computer and mathematical work, which represented 37.2% of queries, while 36% of occupations showed AI use in at least a quarter of associated tasks and about 4% reached three-quarters. The report found 57% augmentation versus 43% automation, lower use in both low- and very-high-wage occupations, and plans for open data and repeated analyses, while noting possible classification errors and coding overrepresentation.
Anthropic Economic Index: Insights from Claude 3.7 Sonnet
Anthropic’s second Economic Index report examines how Claude.ai usage changed after Claude 3.7 Sonnet launched, using 1 million anonymized Free and Pro conversations from the following 11 days. Clio maps conversations to 17,000 tasks in the U.S. Department of Labor’s O*NET database, enabling comparisons across occupations and interaction modes, while the report also introduces a 630-category bottom-up taxonomy. Coding, education, and science usage increased modestly, and extended thinking appeared most often in technical and creative work, including computer science research, software development, multimedia art, and video game design. Augmentation remained 57% of usage, learning interactions rose from about 23% to 28%, and the report releases task- and occupation-level datasets covering thinking mode and augmentation-versus-automation patterns.
How we develop pricing and packaging at Fin
Pricing and packaging at Fin is treated as a cross-functional research, forecasting, and strategic problem rather than a finishing step. When new monetization is needed, the process starts with qualitative buyer research to define a pricing model and metric; for Fin, that produced value-based pricing tied to “outcomes,” each defined as the AI Agent successfully handling a customer service query. Quantitative willingness-to-pay studies using Gabor-Granger and Van Westendorp then indicate demand across price points, but do not establish a perfect price. Modeling combines those signals with discounting, usage, segment variation, attach rates, margins, sales capacity, competitor pricing, and finance forecasts to recommend a price alongside ARR and adoption scenarios before executive sign-off. The approach is iterative and applied selectively, while expanding products and capabilities create an ongoing need to review whether the pricing system remains coherent and aligned with delivered value.
Data Science vs Data Engineering: Choosing Analysis or Infrastructure
This guide distinguishes data engineering from data science by their roles in the data value chain. Data engineers build and maintain ingestion pipelines, warehouses, lakes, access controls, and production systems, using tools such as SQL, Python, Scala, Java, Apache Spark, Kafka, orchestration platforms, and cloud infrastructure. Data scientists work with clean, accessible data to perform exploratory analysis, engineer features, train and validate statistical or machine-learning models, and communicate findings through visualization and storytelling. The roles collaborate through feedback on data quality, shared documentation, model deployment infrastructure, and versioned schemas, feature pipelines, and experiments. For choosing a path, the guide recommends trying a two-week pipeline project and a two-week ML project; it also outlines portfolio deliverables for each and says both roles require strong analytical and computer-science fundamentals.
How Shopify Uses Recommender Systems to Empower Entrepreneurs
Shopify uses recommender systems to help merchants find relevant insights, actions, apps, themes, services, and other resources across its commerce platform. Its approach represents implicit signals such as purchases, installations, clicks, and views as user-item interactions, then applies binary classification and linear methods including SLIM and LRec to learn an item-item similarity matrix and rank unseen items. The framework supports task-parallel training, interpretable coefficients, user-feature enrichment, and cold-start handling for new users or items, while evaluation considers precision alongside diversity, novelty, serendipity, and business-specific goals. In initial A/B tests, personalized app recommendations produced a 50% higher install rate, home feeds were up to 12% more likely to be rated useful, expert matching increased response and collaboration, and theme recommendations made merchants over 10% more likely to launch stores.
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