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Data Scientist

at Thumbtack in San Francisco, CA   —   Jun 24, 2014   |  
There are only two kinds of truth in the world: philosophical truth and statistical truth. Philosophical truth draws on pure reason; statistical truth draws on data. Philosophical truth can be hard to apply to business, but statistical truth lies at the very heart of Thumbtack. Our collective reverence for the power of statistical analysis is, we believe, one of our key advantages as a business and one of the primary drivers of our success.

What is a "data scientist", anyway? Well, it's "data" and "scientist":

Data: "Only the data are real". Unfortunately, most data are extremely unwieldy. Drawing correct conclusions from data requires a sharp analytical mind, a deep understanding of the fundamentals of statistics, and a broad toolbox of statistical and machine learning techniques. It also requires the engineering skill to wrangle data from various sources and to implement computationally-challenging algorithms over large data sets.

Scientist: A scientist practices the scientific method: formulating hypothesis, testing them, and drawing inspiration for new hypothesis from the results. This requires the scientist to use both sides of her brain in tandem, creatively coming up with useful hypotheses and analytically testing, rejecting, revising and expanding them in a continual interplay. A great scientist doesn't simply analyze data to answer a question. A great scientist finds the incredibly valuable questions no one has considered.

We're looking for a brilliant individual to form the cornerstone of our data science team. Our process gives you full ownership over the projects you tackle: so dream big, then execute well.

You'll be given the latitude to survey Thumbtack as a business, identify key opportunities, and use any and all available data to draw actionable conclusions that will guide the direction of the company.
You'll design, implement and launch experiments to test your hypotheses
You'll identify and implement metrics that align with company goals
You'll contribute to our Python codebase and perform analyses in R or another statistics tool of your choice
You'll advise coworkers on the (mis)use of statistics to understand data
You have a deep understanding of probability and statistics, including (but obviously not limited to) experimental design, randomization and blocking, error types and loss, frequentist vs. Bayesian approaches, bootstrapping and simulation, modeling (including techniques for nonlinear responses, generalized linear models, and model selection and evaluation), confounding and effect modification.
You're proficient with statistical analysis in an environment/tool/language of your choice (we use R and Python/Pandas currently) and comfortable creating repeatable, automated analyses.
You're comfortable writing efficient code to handle large data sets (on a single machine).
You express yourself clearly and concisely in written and verbal discussion of complex problems.
This job description resonates with you.
Competitive salary and benefits

Please apply here:

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