mailitics

Tag: product

  • The top 5 most common product analytics case interview questions asked in big tech interviews

    The top 5 most common product analytics case interview questions asked in big tech interviews Hey folks, You might remember me from my previous posts about my progression into big tech or my guide to passing A/B Test interview questions. Well, I’m back with what will hopefully be more helpful interview tips. These are tips…

  • My experience after final round interviews at 3 tech companies

    My experience after final round interviews at 3 tech companies Hey folks, this is an update from my previous post (here). You might also remember me for my previous posts about how to pass product analytics interviews in tech, and how to pass AB testing/Experimentation interviews. For context, I was laid off last year, took…

  • EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas

    EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales data. The post EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas appeared first on Towards Data Science. Ibrahim Salami Go to original source

  • A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play

    A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play What a simple puzzle game reveals about experimentation, product thinking, and data science The post A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play appeared first on Towards Data Science. Yu Dong Go to original source

  • The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation

    The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation How product, growth and engineering teams can converge on a single signal for better incident management The post The Product Health Score: How I Reduced Critical Incidents by 35% with Unified Monitoring and n8n Automation appeared first on…

  • From data scientist to a new role ?

    From data scientist to a new role ? Hi everyone, I’m 25, currently working as a Data Scientist & AI Engineer at a large Space company in Europe, with ~2.5 years of experience. My focus has been on LLM R&D, RAG pipelines, satellite telemetry anomaly detection, surrogate modeling, and some FPGA-compatible ML for onboard systems.…

  • Does meta only have product analytics?

    Does meta only have product analytics? I have been told that all meta data scientists are all product analysts meaning that they do ab tests and sql. Despite this, i ve been told by friends of mine that google, amazon, uber… they all have two different types of data scientist: one doing product analytics and…

  • Can you explain to me the product analytics job?

    Can you explain to me the product analytics job? I ve watched videos about Data Scientist Product Analytics but i still dont understand if the job would excite me. Can someone explain it more in depth so that i can understand if i like it? I like the data science job (i am pursuing a…

  • Estimating Product-Level Price Elasticities Using Hierarchical Bayesian

    Estimating Product-Level Price Elasticities Using Hierarchical Bayesian Using one model to personalize ML results The post Estimating Product-Level Price Elasticities Using Hierarchical Bayesian appeared first on Towards Data Science. Derek Tran Go to original source

  • Dynamic Assortment Selection and Pricing with Censored Preference Feedback

    Dynamic Assortment Selection and Pricing with Censored Preference Feedback arXiv:2504.02324v1 Announce Type: new Abstract: In this study, we investigate the problem of dynamic multi-product selection and pricing by introducing a novel framework based on a textit{censored multinomial logit} (C-MNL) choice model. In this model, sellers present a set of products with prices, and buyers filter…

  • Evolving Product Operating Models in the Age of AI

    Evolving Product Operating Models in the Age of AI In a previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of outcomes, outsourcing of staff, and the geographical proximity of team members — can yield a variety of organizational archetypes for implementing strategic AI initiatives,…

  • The Basics you Must Master Before Diving into Marketing & Product Analytics

    The Basics you Must Master Before Diving into Marketing & Product Analytics Things that still confuse many Data Analysts Recently, I gave a presentation on a specific topic: how to investigate drop-offs in conversion funnels within the context of marketing and product analysis. What surprised me? The incredible engagement from the audience. The questions were varied…

  • Measuring Cross-Product Adoption Using dbt_set_similarity

    Measuring Cross-Product Adoption Using dbt_set_similarity Enhancing cross-product insights within dbt workflows Introduction For multi-product companies, one critical metric is often what is called “cross-product adoption”. (i.e. understanding how users engage with multiple offerings in a given product portfolio) One measure suggested to calculate cross-product or cross-feature usage in the popular book Hacking Growth [1] is…