Overview

At Luxe, members of the Data Science group collaboratively design algorithms to permit the business to scale efficiently. Examples of problems that we are tackling are:

-ETAs

-Dispatch

-Pricing

-Valet staffing

-Customer retention

Each project requires some amount of:

-performing ad hoc analyses to see where opportunities lie

-standardizing definitions of metrics we want to affect

-writing portable Python modules that access and transform our data into these metrics, so that anybody at any time can access these metrics programmatically

-devising and implementing scalable algorithms or models that govern real-time decision making

-composing APIs so that the Engineering department can make use of our algorithms’ decisions

-running experiments to tune the parameters of each algorithm or model, in order to move our standardized metrics.

We’re hiring for the following roles. Whatever role you get hired into at first, there is a good chance that you’ll be trying on all of the hats over time, depending on our needs and your desires.

Pythonic Data Analyst

A Data Analyst will help build our library of rigorous metrics. Building a metric entails ad hoc exploration followed by standardizing it into an airtight python module. Some metrics will be used in algorithm development, while others will be the basis of charts and graphs for consumption by Product and Operations.

You will:

-have skills in Python and pandas

-have really good SQL skills

-have an academic background in a STEM field

-understand the tenets of building and statistically evaluating models

Pythonic Data Scientist

Data Scientists at Luxe use Machine Learning, Graph Theory, Statistics, Queueing Theory, or whatever other tools they can cobble together to design and implement predictive models (such as ETAs) and optimize algorithms (such as Dispatch and Pricing) that promote our growth and sustainability

You will:

-have an advanced background either in stats, math, engineering, CS, etc

-have good Python skills -have created models using the ML algorithms in scikit-learn, understanding trade-offs between different algorithms

-be excited for the opportunity to develop algorithms (such as Dispatch, ETAs, Pricing) that can be rigorously evaluated against our metrics

-be expected (at least eventually) to produce APIs so that the Engineering group can interact with our algorithms

Data Architect and/or Pythonic Data Engineer

On one face of the Data Science group is a Redshift database, to which we log lots of data to document our real-time decisions, and from which we read lots of data for historical analysis. On a different face, APIs read the state of the world from, and send decisions to, the Engineering group. We are looking for an Engineer who is well-versed good data architecture practices, and good API design, and who has worked alongside or within a Data Science group before.