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Data Science at Companies

Why does Airbnb have a data scientist on every team? What did it take to build out Thumbtack's data infrastructure? How do Twitch data scientists convince execs to embrace data-informed decision making? Get behind-the-scenes perspectives on how data teams at actual companies tackle questions of building infrastructure, scaling analytics, attaining buy-in, and structuring teams.

The Big Four Reasons Companies Struggle to Hire Data Talent

We hear from both sides of the data talent market from the thousands of data scientists, analysts and others who use Mode every day. Here are four common problems we’ve noticed companies face when hiring for data talent, and how you might fix them. - Mode

Doing good data science

“Moving fast and breaking things is unacceptable if we don’t think about the things we are likely to break. And we need the space to do that thinking: space in project schedules, and space to tell management that a product needs to be rethought.” - O’Reilly

Apple is rebuilding Maps from the ground up

Google Maps’s moat may dry up soon. Apple Maps is moving from relying on third-party data to owning all of the data that goes into making a map, without compromising their firm stance on protecting user privacy. - TechCrunch

3 Weird Analytical Practices at Mode

We offer a peek into Mode’s internal analytics culture, including the fact that the most technically proficient folks actually sit on our Customers Success team. - Mode

Reporting is a Gateway Drug

Some advice on how to use reporting as a means to create strong stakeholder relationships in your organization. - Locally Optimistic

Data Violence and How Bad Engineering Choices Can Damage Society

“If you have the temerity to insert your work into a political issue that… doesn’t immediately affect your life, you should also be prepared to accept the consequences—or, at the very least, answer a few hard questions.” - Medium

How to Attract Top Data Science Talent: Lessons from 1,400+ Insight Fellows

The head of Insight Data Science shares three key areas for differentiation to focus on throughout the hiring process. - Mode

How Grubhub Analyzed 4,000 Dishes to Predict Your next Order

Grubhub had 14 million menu items and the only thing they had in common was that sometimes people ate them. Here’s how their data team built their own taxonomy of food. - Wired

Use These Data Analytics Tips to Find Your Film’s Audience

The film industry is years behind others—like interactive and music—with regards to access to data. This post breaks down how filmmakers can collect data at all times throughout their art-making to build audiences and maximize revenue. - Sundance Institute

The (Data Science) Notebook: A Love Story

Speaking of which… what’s driving the rapid adoption of the notebook interface as the preferred environment for data science work? - Mode

Data-Driven or Insights-Driven? Data Analytics vs Data Science

This is a great example of how to handle objections that come up in conversations with folks who aren’t entrenched in data all day. - Jen Stirrup

8 Great In-App Analytics Pages in B2B Software

You probably have data that can help your users do their jobs better and frame your business in a positive light. Here are some great examples of companies providing valuable usage data to their customers through in-app analytics. - Mode

How to Create a Great In-App Analytics Page

If you’re kicking off a project to build your own in-app analytics, keep these six considerations in mind. - Mode

How Mode’s User Stats Page Answers Customer Questions While Increasing Feature Use

Looking for a way to highlight underused features? Are customers hounding you about the value they’re getting out of your product? A usage page might just kill those two birds with one stone. - Mode

How Quora’s Head of Data Science Conducts Candidate Interviews

Anyone who has considered leaving the ivory tower should give this a read. - Mode

Scaling Event Tables with Redshift Spectrum

As Mode’s customer base grew, we reached a point where our infrastructure wasn’t capable of handling the exponentially increasing volume of event data. Here’s how we saved Redshift performance by offloading 75% of our event data to S3 in less than a week. - Mode

So here’s my postmortem after hunting for a data science job

“I’m tired of all the Medium thought pieces on how to become a data scientist because they don’t reflect the reality of getting a relevant job from the applicant’s side. And it’s hard, especially without a Masters/PhD.” - Max Woolf

Transitioning From Academia to Industry: Perspectives from Indeed’s Data Scientists

Eric Mayefsky has assessed hundreds of job candidates in his half decade in management at various tech companies. Here are the five key lessons that have helped him build an amazing data team. - Indeed Data Science

One Year as a Data Scientist at Stack Overflow

Lots of great reflections in here about being a data scientist at a small company, the R workflow, and working remotely. We especially love this: “Data science is highly technical work, but the value of my technical work would be much lower if I could not communicate what it means in clear and compelling ways.” - Julia Silge

Recommended companies for early-career data scientists

This is a goldmine for junior data scientists looking for companies with defined career paths and mentorship opportunities. - Hilary Mason

Directions of Ascent

What can individuals who work with data and code do to “fix the mess that tech has enabled”? - arg min blog

Google Maps's Moat

Twitter user @msquinn put it best: “This is a great look at how the Google Maps data strategy first conceived over a decade ago continues to unfold in the product you see today.” - Justin O’Beirne

Bridging the Trust Gap: Data Misuse and Stewardship by the Numbers

Consumers don’t understand how companies actually use their data. Meanwhile, companies think more consumers understand data stewardship practices than actually do. - The Boston Consulting Group

Engaging the Ethics of Data Science in Practice

“The critical writing on data science has taken the paradoxical position of insisting that normative issues pervade all work with data while leaving unaddressed the issue of data scientists’ ethical agency. Critics need to consider how data scientists learn to think about and handle these trade-offs, while practicing data scientists need to be more forthcoming about all of the small choices that shape their decisions and systems.” - Association for Computing Machinery

Data Meta-Metrics

How do you communicate confidences and doubts about data to a non-technical audience? Check out one analyst’s method for adding a “state of the data” aspect into her presentations to get the whole team involved in the data improvement process. - Caitlin Hudon

Communication in data science

When people stress the importance of good communication in data science, they're usually talking about communicating results—the last step in a data scientist's workflow. But communication is more than just a final bottleneck. It’s important at every stage. - University of British Columbia

Corporate Data Science

A great talk for those tasked with growing a data science team, covering how to optimize a team's makeup across many dimensions and instill in them the importance of caring deeply about data collection, security, ethics, and interpretability. - Angela Bassa

Avoiding Being a 'Trophy' Data Scientist

A collection of the challenges data scientists face in their quest to add value to a company. - Peadar Coyle

Big Data Processing at Spotify: The Road to Scio (Part 1)

Using Scio, a built in-house Scala API, Spotify is able to run the majority of their workloads with a single system, with little operational overhead. - Spotify Labs

The Four Cringe-Worthy Mistakes Too Many Startups Make with Data

HotelTonight's Chief Data and Strategy Officer shares why you should run your data team like product and think twice before hiring a data scientist. - First Round Review

How to Job Interview a Data Scientist

Mode CEO Derek Steer explains where most data scientist job interviews fall short and three key criteria for evaluating candidates. - Mode

From Power Calculations to P-Values: A/B Testing at Stack Overflow

If you're a little fuzzy on the relationships between sample size, effect size, false positive, and false negative rates, this post and the accompanying interactive calculator will clear things up. - StackOverflow

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

“In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment’s outcome, which if deployed could hurt the business by millions of dollars.” - Microsoft

ZATA: How we used Kubernetes and Google Cloud to expose our Big Data platform as a set of RESTful web services

An inside look at zulily's data platform, which makes data accessible to analysts, systems, and applications without sacrificing speed or storage options. - Tech @ zulily

Landing a Data Science Gig in New York City

Trying to break into the NYC data science job market? Sans a PhD? This guide was tailor-made for you. - Ground Truth

How Stitch Consolidates A Billion Records Per Day

Ever wanted to know how the people who make ETL tools set up their data infrastructure? Wonder no more. - StackShare

5 Ways to Make Sure Your Analytics Spark Growth

From setting the right KPIs to sending events to the right place, here are the steps every company should take to make their product and marketing analytics worthwhile. - Astronomer.io

Data Science for Fraud Detection

A primer on the inner workings of fraud analysis, from dimensionality reduction to anomaly detection. - codecentric

Data Security for Data Scientists

The Equifax breach is yet another reminder that data security is no longer a niche speciality of database admins and network engineers. Here are 10 suggestions for ensuring the data you work with is properly protected. - Andrew Therriault

Train, Score, Repeat, Watch Out! Zillow's Andrew Martin on modeling pitfalls in a dynamic world.

One of Zillow's data scientists addresses the challenges that don’t crop up in standard textbook problems or most ML competitions: feedback loops, dynamic datasets, and temporal consistency. A great read for Kagglers and non-Kagglers alike. - No Free Hunch

The Data Trust Gap and How to Close It

78% of people have trouble trusting companies with their data; that means if your customers do trust you with their data, you have a competitive advantage. How do you get there? By making sure data transparency isn’t an afterthought. - InVision

Data Science without Borders

In his JupyterCon keynote, Wes McKinney makes the case for a shared infrastructure for data science, discusses the open source community’s efforts on Apache Arrow, and offers a vision for seamless computation and data sharing across languages. - O'Reilly

Switching to a Probabilistic Model for Venue Search in Foursquare

How Foursquare’s engineering team improved the accuracy and user experience of their location intelligence by switching from a search ranking algorithm to regression trees and probabilities. - Foursquare Engineering

Scaling Analytical Insights with Python (Part 1)

FloSports' VP of Product shares the subscriber retention analysis that allows their business to move quickly as the number of data sources and volume of data increases. - Kevin Boller

Choosing an ETL tool for your analytics stack

In the market for an ETL solution? Here's the criteria we employed when we evaluated ETL vendors for our own use here at Mode. - Mode

Cargo cult data science

A “cargo cult” emerges when people copy a set of behaviors without understanding the “why” behind them. Many data science projects fail because they focus on implementing the technical side without pushing for a cultural shift to become data-driven. Here's how to avoid that pitfall. - Richard Weiss

Data Science: Challenges and Directions

Carve out some time in your schedule for this academic paper on “the gaps between the world of hidden data and existing data science immaturity.” It's a dense but rewarding read. - Communications of the ACM

Timing is everything: what our data says about the best time to send a message

Intercom analyzed millions of in-app and email messages from B2B companies to figure out when to send messages so they'll reach the maximum amount of people possible. This is a cool example of turning product analytics into actionable insights for customers. - Inside Intercom

I have data. I need insights. Where do I start?

What to do when your boss dumps a bunch of data in your lap and says “tell me something interesting.” - Towards Data Science

You Say Data, I Say System

Every spreadsheet or database view or visualization is the result of an entire system of decisions: how to collect, compute, and represent the data. This article provides an excellent framework for being mindful of the choices that shape the end product you see on your screen. - Hacker Noon

Rise of the Data Product Manager

“Working with data at the core of a product requires… a deep appreciation for what is possible and what will soon be possible by taking full advantage of the flow of data.” - Trey Causey

How To Get People to Value Your Analytics work — the Principle of Scarcity

It doesn’t matter if you deliver the best analyses in the world if you can’t persuade people to make use of those analyses. - Jon Tesser

How R Powers Data Science at Microsoft

“Every application we have today… can be made more intelligent by having a layer of R in the middle.” - insideBIGDATA

Two years as a Data Scientist at Stack Overflow

Two years in, David Robinson chronicles hiring a second data scientist, teaching R to developers, and writing production code. - Variance Explained

Getting started: the 3 stages of data infrastructure

Setting up data infrastructure no longer feels like 'trying to build a skyscraper using a toy hammer.' Nowadays there are so many options that assembling a data stack is downright daunting. Take a deep breath, and start here. - Nate Kupp, Thumbtack

Athos Changes the Game

Athos is revolutionizing sports performance with muscle activity based data. Learn how this smart startup uses Mode for fast access to business intelligence. - Mode

How to Hire a Product Analyst

6 things to look for in a product analyst + 14 questions you can use to tell a good candidate from a great one. - Amplitude

The Algorithms Behind Moana’s Gorgeously Animated Ocean

Disney engineers used a series of algorithms to simulate realistic water movement, like splashes, eddies, and wakes. The simulation output millions of new points of animation data that were smoothed into the final rendering of the film. - The Atlantic

New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll

KDnuggets released the results of their 18th annual analytics and data science software poll. TL;DR: Python barely overtook R and Deep Learning usage surged to 32%. - KDnuggets

How Airbnb Democratizes Data Science With Data University

“In order to inform every decision with data, it wouldn’t be possible to have a data scientist in every room—we needed to scale our skillset.” - Airbnb Engineering & Data Science

Methodologies as Vanity Metrics

“When you work on learning new methods (Now I know Random Forest! Now I know K-L Divergence! Now I know Deep Learning!) it feels good—you’re exercising your brain, you know something you didn’t before—and it’s easy to think you’re progressing. But methods don’t in and of themselves drive value.” - Ian Blumenfeld

Spotting a million dollars in your AWS account

“Shooting for 80% completeness (being willing to say 'it's good enough') ended up saving us again and again from rabbit-holing into analysis that didn’t meaningfully impact our spend.” - Segment

How we learn how you learn

Duolingo leveraged their product data to create a new statistical model for effective language learning. - Making Duolingo

Building Data Science Teams

Should data science teams be standalone or embedded or integrated completely? How should a company conduct their data science hiring process? Instacart's VP of data science shares their secret sauce for keeping data folks happy and productive. - Jeremy Stanley

Data Driven Products Now!

Here's Etsy's exact process for validating new products and features with data, before starting in on the development process. - Dan McKinley

The Key to Growth? Illuminating Your Best Bets

How Airbnb and Facebook identified their North Star metrics for growth. - Startup Grind

Reliable export of Cloud Pub/Sub streams to Cloud Storage

Here's how Spotify's data infrastructure team set up an event delivery system to handle over 100 billion events generated each day. - Spotify Labs

7 Disruptive Trends to Watch For in Analytics in 2017

A wide-sweeping look at why data tools are changing to address problems like governance, collaboration, and backlogs of data requests. - Graphiti XYZ

Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest

Learn how tech behemoths store and process petabytes of interactions, page views, and customer data. - Keen.io

Data Minds Episode 6

Mode's CEO talks the most common misconceptions about analytics, the skills that data people need to foster to become team leaders, and how the data team can make the case to invest in the tools they need. - Data Minds

Using dbt and Mode to Help Eko Rebuild its Analytics Stack

Learn how interactive video player Eko built a new data modeling layer and kept data consistent across internal and external reporting. - Mode

How to stay out of analytic rabbit holes: avoiding investigation loops and their traps

“[O]ver-analysis begins when the data scientist starts focusing on the hypothesis instead of the decision.” Here's how to tell the two approaches apart and avoid analysis paralysis. - cyborgus

How You Battle the "Data Wheel of Death" in Growth

“Most companies that want to get more serious about data approach it as a project. Something with a definitive start and a definitive end. In reality, data is an ongoing, never-ending project, similar to building a product.” - Coelevate

Custom Data Visualizations in the Workplace

ICYMI, here's a recap video of of presentations from data team leaders at Envoy, Good Eggs, and Thumbtack on their highest-impact work, how projects are conceived and received, and tips for identifying when to to invest in custom data viz. - Mode

The Startup Founder’s Guide to Analytics

“How do I build a business that produces actionable data?” is much harder to answer than “What metrics should I track?” Here's a quick start implementation guide based on the growth stage of your company. - ThinkGrowth.org

Data Science On The Silicon Beach

In this interview, the Chief Data Officer for the city of San Diego discusses his team’s ad-hoc approach, integrating their stack with legacy systems, and his plans for employing data to alleviate traffic congestion. - Partially Derivative

Hiring a data scientist

Hiring for a data analyst is no easy task. Wikimedia shares how they drew on existing resources to synthesize a better approach to interviewing and hiring a new member of their data team. - Wikimedia

How Fitbit’s data science team scales machine learning

Workout regimens need to be tailored to each individual. Directional correctness isn’t enough. Fitbit’s head of data science shares how his team builds a model for every user to increase motivation and prevent injuries. - Mixpanel

Scaling Data Science at Stitchfix

Not many companies can say they employ 80 data scientists. The folks at Stitchfix share their tactics for making data and compute resources more accessible—which in turn keeps data scientists happy and infrastructure healthy. - MultiThreaded

Building & Maintaining a Master Data Dictionary: Part 2

Check out these ideas for structuring key metric definitions to keep everyone at your organization on the same page. - The Data Point

What’s it like to work in sports analytics?

From the outside, crunching numbers for a national sports league seems glamorous. The cold hard truth? It’s an often thankless job with low pay and long hours. The only thing that’ll prevent burnout is a pure love of the game. - StatsbyLopez

Quora Session with Monica Rogati

The Former VP of Data at Jawbone did a Quora session last week. - Quora

How To (Actually) Calculate CAC

Quick: What’s the difference between customer acquisition cost (CAC) and cost per acquisition (CPA)? If you hesitated, this post is for you. - Brian Balfour

Breaking the Vanity Metric Cycle

“[B]reaking free of worthless metrics is hard because it is breaking a psychological reward, not just adopting some new stats.” - Amplitude

Trust in Data Science

“An untrusted analysis is an unused one, regardless of the quality. So how does one go about building, or rebuilding, trust in the face of challenges and failure?” - Clover Health

The Limitations Of Data And Benchmarks

“All the quantitative analysis in the world won’t lead me to the next great idea for startup. Those figures can’t create empathy, develop the right culture, or hire the right people.” - Tomasz Tunguz

8 Data Science Skills That Every Employee Needs

A nice primer to share with your colleagues. - Amplitude

Ten Ways Your Data Project is Going to Fail

“Many companies seem to go through a pattern of hiring a data science team only for the entire team to quit or be fired around 12 months later. Why is the failure rate so high?” - Martin Goodson

Why I’m Teaching Twitch to Predict the Future

Forecasting is a good habit to adopt in the workplace. It’ll help you figure out the odds of delivering on your goals. Plus, having a record of accurate predictions builds trust in your work and analytical thinking in general. - Twitch

Tracking Customer Service Metrics With SQL

This guide includes a dozen SQL queries for calculating customer service metrics with raw Intercom data. - Mode

Data Literacy, Product Design and the Many-Faced God

“Building a team that’s doing ‘cutting-edge research in deep learning, machine intelligence, and artificial intelligence’ is not easy—not in this hiring environment. But infusing data thinking throughout a company is orders of magnitude harder. This matters, because data thinking permeates your products and can make them feel ‘smart’—or not.” - Monica Rogati

Don’t Become a Victim of One Key Metric

“[T]he search for one key metric at all for a complex ecosystem like Pinterest over-simplifies how the ecosystem works and prevents anyone from focusing on understanding the different elements of that ecosystem. You want the opposite to be true.” - Casey Winters

GGPlot2 As a Creativity Engine, and Other Ways R is Transforming the Financial Times Data Journalism

Learn how the Finanical Times produces high-quality data visualizations in this presentation, complete with the R code and data used for their piece, Explore the changing tides of European footballing power. - Financial Times

Surviving Data Science “at the Speed of Hype”

Complex optimization models work best when they’re asked to deal with stable business problems, like airline scheduling or ad targeting at Google. But at a startup, where the business model is constantly changing, simply summarizing data is a much better way to find answers. - John Foreman

How We Rebuilt the Wall Street Journal’s Graphics Team

The WSJ used to have two Graphics teams—one for print and one for the web. Combining the two has allowed editors to focus on storytelling from the start of projects, instead of the medium. - Source

What I Wish I Knew About Data For Startups

One entrepreneur reflects on his learnings from four years of working with data at a startup. It’s a goldmine of advice on building a strong, scaleable data culture. Don’t skip this one. Seriously. - Jean-Nicholas Hould

Simple requirements gathering questions for dashboard design

Next time someone asks you to make a dashboard, pull this list out. It provides a framework for sussing out what’s needed for the dashboard to be useful and effective. - Paint by Numbers

FiveThirtyEight’s data journalism workflow with R

FiveThirtyEight’s quantitative editor shares the analytical process behind some of their publication’s most popular articles. - useR!

The Data Driven Daily

This newsletter provides definitions of business KPIs, how to calculate them for your business. This week they’re covering how to determine the size of your potential customer market. The archive is well worth perusing; past segments include revenue calculation and pricing strategy. - Outlier

Practical advice for analysis of large, complex data sets

“This document has been read more than anything else I’ve done at Google over the last eleven years. Even four years after the last major update, I find that there are multiple Googlers with the document open any time I check.” - The Unofficial Google Data Science Blog

One year as a Data Scientist at Stack Overflow

The chronicle of one data scientist’s transition from academia to the tech industry, combined with a peek into Stack Overflow’s machine learning and data infrastructure projects. - David Robinson

Whom the Gods Would Destroy, They First Give Real-time Analytics

Every few months, I try to talk someone down from building a real-time product analytics system. When I'm lucky, I can get to them early. - Dan McKinley

Real-time dashboards considered harmful

There’s a certain allure to real-time data: your team can see what’s happening right now and take action immediately. Ultimately, though, most real-time dashboards create a bunch of noise that distracts you from more important metrics. - Basecamp

Boosting Sales With Machine Learning

One developer shares how his team used natural language processing and machine learning in Python to pre-qualify sales leads so reps don’t have to spend hours doing it manually. - Xeneta

Scaling Knowledge at Airbnb

Airbnb’s data team shares their solution to ensuring insights don’t get lost in Google docs or email threads: a centralized knowledge repository. - Airbnb Engineering

Bridging the Gap Between Data Science and Data Engineering

Josh Wills, Director of Data Engineering at Slack, shares his thoughts on how data engineers and data scientists work best together. - Hakka Labs

Statistically Interesting

Craving a new data science podcast? Check out Statistically Interesting, a series of interviews with data science leaders at companies like Twitch, Vimeo, and Weebly. - Statistically Interesting

How to Make Reps Care About Data Quality

When a sales rep fails to record information about her activities or clients, it can lead to incomplete and inaccurate reports and forecasts. These tips and tricks will help sales leaders encourage reps to be vigilant about consistently logging data. - InsightSquared

The View From The Data

Making data-informed decisions has a lot more to do with people than it does with the actual data. - Karen Roter Davis

Building Data Science in Healthcare

Many tech companies have complete control over the format of the data they collect. Healthcare, which relies on external data about patients and their interactions, has no such luxury. Ian Blumenfield, Head of Data Science at Clover Health, shares how they handle messy data and the other unique data challenges the industry faces. - Clover Health

This Is How You Build Products for the New Generation of ‘Data Natives’

We’ve grown used to the idea of digital natives—the toddler who expects everything to be a touchscreen and pinches and swipes her fingers on TVs and magazines. But data natives are something different: they expect “their world to not just be digital, but to be smart and to adjust immediately to their taste and habits.” Monica Rogatti, former VP of Data at Jawbone, shares ideas for harnessing data to build products for these new consumers. - First Round Review

Hot property: How Zillow became the real estate data hub

Zillow is a real estate powerhouse, and one of their biggest competitive advantages is their massive dataset of property listings. The most interesting part of this article goes into how their data science team brings together messy data from disparate sources to create one coherent super-dataset. - InfoWorld

So you want to build a data business? Play the long game

Foursquare has demonstrated, once again, that it’s capable of predicting public company earnings with an incredible degree of accuracy based on real world foot traffic data. - Michael Carney

The Art and Science of Storytelling Through Data at Jawbone

Analysis can be worthless if it’s not communicated well. Jawbone data scientist Kirstin Aschbacher shares how she develops a data story that inspires action, from concept to presentation. - Insight Data Science

Doing Data Science Right — Your Most Common Questions Answered

This is a must-read for startup founders who want to build data science teams. It’s packed with details on the inner-workings of data-driven businesses and advice on where to start based on your company’s needs. - First Round Review

How Does the Data Science Team Work at Twitch?

In this interview, Twitch's Director of Science shares how the data science team thinks about mentorship, gaining leverage, and qualitative research. - Mode

Analyzing Your Stripe Data, Part 1: Measuring Subscription MRR

Got raw Stripe data? Want to calculate your subscription monthly recurring revenue? Lucky for you, this post provides the SQL queries you’ll need, tips for data prep, and ways to tailor the analysis to your business. - Analyst Collective

Riley Newman on Data Science for Startups

In this interview, Airbnb’s head data scientist Riley Newman talks about building a strong data culture, balancing technical skills with storytelling ability, and scaling data science at a high-growth startup. - Intercom

Building a high-throughput data science machine

Scaling is a problem every data science team faces. How do you go from one nomadic analyst roaming between departments to a structured team? The answer is a little different for every company, but this interview introduces some best practices to keep in mind. - O’Reilly

How to Find Correlative Metrics For Conversion Optimization

A thorough walk-through of how to find correlative metrics and leverage them for conversion. It’s jam-packed with examples and advice from experts, plus a handy list of tools. - ConversionXL

Why Airbnb Has a Data Scientist on Every Leadership Team

Airbnb's head of data science shares his keys for success in data and business. - Inc.

How does Lumosity use data science?

An inside look at the structure of Lumosity’s data science team and the internal tools and product features they build. - Quora

CAC Payback Period: The Most Misunderstood SaaS Metric

If you’re calculating customer acquisition cost payback period for a SasS product, keep these two things is mind: payback metrics are about risk, not return, and that most SaaS products operate on an annual model, not monthly. - Kellblog

The Five-Step Guide to Robust Help Center Metrics

When a documentation manager set out to revamp her company’s help site content, she was surprised to find very few resources on how to measure her project. Thankfully, she documented her journey so we can all learn from it. Great tips in here for anyone looking to make their help center more, well… helpful. - RJMetrics

How to catch million dollar mistakes before they cost you millions of dollars

Are you measuring the impact of back-end updates on user behavior? Failure to do so could cost you big time. - Lucidchart

The Role of Statistical Significance in Growth Experiments

When you run an experiment, you’re looking for statistically significant results. But if you’re running growth experiments on a product—iterating quickly to optimize—the standard rules of statistical significance may not apply. - Medium

7 Steps to Measuring the Success of a Feature

You’ve spent months working on a feature and now it’s live. How do you tell if users actually like it? Dig into your user data and start measuring with this detailed walkthrough. - Amplitude

Minimum Viable Onboarding for PMs

A product manager from Doordash shares his thoughts on the most successful employee onboarding process he’s experienced. Spoiler—it involves data analysis. - Charlton Soesanto

What BuzzFeed’s Dao Nguyen Knows About Data, Intuition, And The Future Of Media

This entire article is worth reading, but skip to the middle for the real gem—publisher Dao Nguyen’s holistic philosophy on data at BuzzFeed. “[Y]ou can’t only use comments, you can’t only use data, you can’t only use anything. You can’t only use your own intuition, either. It has to be all of those things you use.” - Fast Company

Highly Effective Data Science Teams

To do great data science work, you need more that a huge heap of data. This article offers 14 criteria for assessing your team’s effectiveness. - Twitch

Diligence at Social Capital, Epilogue: Introducing the 8-ball and 'GAAP for Startups'

Figuring out what metrics to present to investors can be a struggle for startups. That’s because there’s really no standardized metrics or reporting in the startup world. Venture capital firm Social Capital is hoping to change that with their tool for gauging product-market fit at early stage companies. Plug in your own data and give it a whirl. - Jonathan Hsu

Building a business that combines human experts and data science

An insightful interview with Eric Colson about algorithms, human computation, and building data science teams at Stitch Fix and Netflix. - O’Reilly Data Show Podcast

You’re Measuring Daily Active Users Wrong

A high number of daily active users (DAU) may sound impressive, but does it actually mean anything? To make your DAU metric actionable, you need to measure how often users are getting core value out of your product, not how many times they log in. - Amplitude

The Ecommerce Holiday Customer Benchmark

Those new customers from the holiday season are more valuable than you thought. So when should you engage these shoppers to turn them into repeat customers? - RJMetrics

How Toyota Revamped Its Collections Biz with Big Data Analytics

Toyota Financial Services (TFS) used to collect car payments with a one-size-fits-all approach. Then the recession hit. For the first time, 100,000 people a day were behind on their payments. With a massive analytics overhaul, TFS were able to personalize their collection strategies and help 6,000 customers keep their cars from being repossessed. - Datanami

Wrangle Conference 2015

If you didn’t get to attend the Wrangle Conference in October, now’s a good time to catch up. - Cloudera

How Instacart Uses Redshift to Drive Growth

In this interview, Fareed Mosavat, growth PM at Instacart, shares how his team combines behavior, shipping, and fulfillment data to inform product decisions. Check out how his team uses SQL to define internal metrics, conduct A/B tests, and discover how many touches it takes before a user makes their first order. - Segment

Fashion Goes Deep. Data Science at Lyst

Fashion moves quickly. So, too, does the data science that powers e-commerce sites. In this interview, Lyst lead data scientist Eddie Bell shares the ins and outs of their recommendation engine. Learn how his team has tackled the challenges of constantly changing merchandise and kept suggestions fresh using machine learning and image analysis. - Fast Forward Labs

3 Tips for Centralizing Your Analytics Team Structure As You Grow

In practice, centralized analytics teams often report into product while supporting the needs of the entire business. - Mode

Data Down on the Farm

This episode is part of a series about data and food from Andreessen Horowitz. Learn how farmers are using software and analytics programs to monitor crop health and performance, implement agricultural policies, and adopt revenue-focused business practices. - Andreessen Horowitz