|LendUp is looking for a data and analytics engineer to be an early hire as we grow our analytics team. We are a seed stage startup funded by Y-Combinator, Kleiner Perkins, Andresseen Horowitz, Google Ventures, Yuri Milner’s Startfund, Data Collective, Thomvest Ventures and a bunch of awesome angels. Our vision of revolutionizing the billion dollar payday lending market has been featured in CNN, Time, Slate, TechCrunch, AllThingsD, NPR, Venturebeat, Inc. and more.|
LendUp makes loans to working-class Americans who don’t have access to prime credit. Our customers use payday loans and pawn shops, paying high fees to deal with unexpected financial emergencies like medical care, car trouble, family emergencies, or cutbacks in hours. They are good people, and we treat them as such. We help them understand how to use credit better, build a repayment history and credit file, and transition them from the high rates they are paying now to the rates that a prime customer might pay on a credit card.
We are a data-driven company. We are building the most innovative credit models in the world to extend credit as aggressively as possible, while assessing who is a likely candidate for the path we offer out of the payday loan trap and into prime credit.
Our ideal candidate codes in Java, builds software, has played with R, Weka, or Mahout, and is excited about tackling challenging modeling problems with questionable, dirty, and otherwise messy data. If you're looking for an opportunity to solve a real world, multi-billion dollar problem that affects millions of people, here's your chance.
* Experience writing web applications. We are looking someone who can code
on a development team, not just glue scripts together to clean up data.
* You need to be happy writing software in Java. We spend a decent chunk of
our time building tools to and deploying results.
* Basic knowledge of machine learning techniques: you should know the
limitations of boosting or why to limit the growth a random forest. Real
expertise here is a plus.
* Familiarity with systems like R, Weka or Mahout are a plus but not
particularly necessary. It’s less about familiarity with any system
specifically, and more that if you managed to get where you are without
using tools like those, this role might not be the right fit.
* Fluency with common statistical techniques and their limitations. In a lot
of cases we’re making judgment calls about “do I believe this result” or
“what’s really going on here” based on extremely limited, biased, dirty,
and otherwise analytically challenging data.
* Real-world experience completing analytical (statistics, machine learning,
optimization) projects at actual companies.
* Experience at financial institutions is a non-factor.
* A PhD is typically a non-factor. PhD dropouts are totally welcome.
* We want someone who is nerdy and passionate about the work they do. If
you’ve never done a statistical project for your own amusement, you
probably won’t love your job enough to fit in with this team.
* This is a role where you’re going to roll up your sleeves and dive right
in, a role where a result that we arrive at in time and moves the needle
is much more important than a result that is technically perfect or
* If you get a kick out of behavioral economics this is a fun place to work.
WHAT THE ANALYTICS TEAM DOES:
* Credit risk modeling — iterating and deploying our credit model
* Business analytics — measuring the effectiveness of marketing campaigns,
leading indicators of customer behavior, and impact of interventions (e.g.
customer service contacts)
* Site analytics and conversion metrics
* New product development — tracking key performance indicators of the
overall loan portfolio and recommending new directions to the rest of the
team — new groups to market to, A/B experiments on the front end, creative
ways to limit or extend credit risk
* Fraud identification
Basically what we’re imagining is an undergraduate or master’s degree in CS, math, or stats; some across-the-board experience; a year at a startup, in the real world, or doing consulting on the side. Someone who is passionate, nerdy, and loves data.
All positions include a competitive salary, early stage equity package, full medical, dental and vision benefits, unlimited vacation, team lunches and your choice of computer equipment (typically a visit to the Apple Store around the corner). To apply, please email email@example.com with subject line: 'HN: Data & Analytics Engineer'. Feel free to include code samples, open source project contributions, fun facts, or anything you feel would make us want to hire you immediately.