Business Intelligence Engineer - Woot
Woot was the pioneer of the Deal a Day model and even after becoming one of Amazon's minions is somehow allowed to go on trying to sell random stuff to people using snarkiness, silliness, and a liberal dose of good old honest tomfoolery. To do this well, though, we actually need someone to be able to dive into vast swathes of data and suck the marrow of keen insights, interesting trends, and models which can identify what our customers love and hate so they can keep buying this junk and coming back to experience the shenanigans!
So we're looking for a talented, self-motivated Business Intelligence Engineer who's passionate about diving deep into the data to build effective and scalable models to create an addictive, personalized and responsive shopping experience on the web that scales seamlessly across all device types and sizes. As a Business Intelligence Engineer, you will help answer key questions such as "Where will Woot's growth come from in a year? In five? How will our customers react to changes in prices, product selection, and delivery times? Which customer would like this deal the best? What is the best marketing channel to promote a particular deal to our customer base? How many times can we mention pizza in our product imagery before people think we have a problem?"
You will have an opportunity to own the long-term outlook for Woot's business and shape strategic decisions by working directly with the Woot CEO and leadership team. You will be working on complex mathematical problems, with a large element of unpredictability. You will develop new sophisticated algorithms and improve existing approaches based on modern statistical, machine learning, and data mining methods to impact the core business of Woot. A successful candidate will be able to formalize problem definitions from ambiguous requirements, extract insights from big data and develop cutting-edge solutions for non-standard problems. If you can also build a model for jokes that are actually funny, or a pun engine, well that's just icing on the cake!
- Build statistical models to represent the quality of deals sourced based on revenue forecasting at various levels.
- Build statistical models to represent perceived deal quality by each customer and in turn come up with models to personalize deals for individual customers on the website, mobile app and various marketing channels.
- Prototype these models by using modeling languages such as R and in-house Machine Learning tools. A software team will be working with you to transform prototypes into production.
- Create, enhance, and maintain technical documentation, and present to other Scientists.
In order to perform the above responsibilities well, you also need to
- Gather data required for analysis and mathematical model building by writing ad-hoc scripts and database queries.
- Interact with software and business groups to develop an understanding of their business requirements and operational processes.
- Bachelors in Machine Learning, Data Mining, Statistics, Applied Mathematics or a related field, or 5 or more years of equivalent industry experience.
- Three or more years of experience in machine learning with domain knowledge and experience in the following areas: data-driven statistical modeling, discriminative methods, feature extraction and analysis, supervised learning.
- Fluency in a high-level modeling language such as MATLAB and R.
- Strong communication and data presentation skills
- Demonstrated ability to own projects end-to-end and work on cross-functional teams
- Experience with large data sets (10 million+ rows).
- A natural curiosity and desire to learn.
- Ability to convey rigorous mathematical concepts and considerations to non-experts.
- Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.
- Experience with programming languages such as Python, Java, C++
- Knowledge of relational databases (SQL).
- Applied experience building/evaluating predictive models is desirable.
- A good understanding of analysis of algorithms and computational complexity is desirable.
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