Data Scientist for Machine Learning, Conversational AI 867 views

Job Description

Location: Vienna, VA

Capital One is seeking a Data Scientist for an exciting initiative to develop multichannel natural language intelligent assistants that provide an outstanding experience for our customers.  So far we’ve brought to life Eno, Capital One’s intelligent assistant, and our Alexa and Cortana skills. We’re quickly improving these and adding more channels. You will work closely with our product, design, and tech teams to develop machine learning models leveraging rich data to bring greater intelligence and humanity to our conversational AI.

At Capital One, data is at the center of everything we do. When we were a tiny startup in 1988,  we disrupted the credit card using an information-based strategy to individually personalize every credit card offer using statistical modeling and relational databases. Fast-forward to the present and our passion for data have skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

Twenty-five years after Capital One was started it’s still led by its founder who is committed to making it a leading tech company, not just a leading bank. Be ready to join a community of the smartest people you’ve ever met, who put the customer first, and want to use their data skills to change banking for good.

As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in distributed computing technologies and operating across billions and billions of customer transactions to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.  You’ll also be part of a strong community of hundreds of data scientists doing industry-leading work.

In this position you’ll spend time:

• Writing code, probably Python, to create machine learning models from conception through retraining, partnering with tech teams to deploy to our 30+ million customers in production
• Mapping whitespace.  You’ll have a lot of latitude to create your own research agenda while focusing on where your team can provide the best value.
• Building partnerships with other ML teams to help deliver more, faster, better

On any given day you may also find yourself:

• Using keras, Tensorflow, and/or other hot tools for prototyping and building robust, deployable models
• Collaborating with our AI teams to make Messaging ever smarter
• Brainstorming with our Design teams to dream up the next cool way to make Messaging more empathetic
• Using Spark and related tools to manage the analysis of billions of customer transaction records
• Writing software to clean and investigate large, messy data sets of numerical and textual data
• Integrating with external data sources and APIs to discover interesting trends (NOAA Weather Data + Credit Card Transactions = Fascinating!)
• Designing a rich data visualization to communicate complex ideas to customers or company leaders
• Reading up on a new modeling algorithm that the industry is starting to use

The Ideal Candidate will be:

• You ask why, you explore, you’re not afraid to blurt out your crazy idea. You probably have a diploma and an impressive GPA, or you dropped out of college, taught yourself and routinely win Kaggle competitions.
• You know how to move data around, from a database or an API, through a transformation or two, a model and into human-readable form (ROC curve, Excel chart, map, d3 visualization, Tableau, etc.). You probably know Python, Java, R, Storm, Julia, SQL, Matlab, Mahout, or think everything can be done in a Perl one-liner.
• Do-er. You have a bias toward action, you try things, and sometimes you fail. Expect to tell us what you’ve shipped and what’s flopped.
• Big, undefined problems and petabytes don’t frighten you. You can work at a tiny crack until you’ve broken open the whole nut.

Basic Qualifications:

• At least a Master’s Degree and some interesting projects OR a Bachelor’s Degree and a minimum of 2 years of experience
• At least 2 years of experience in open source programming languages for large-scale data analysis
• At least 2 years of experience with machine learning
• At least 1 year of experience with relational databases
• At least 1 year of experience with SQL

Preferred Qualifications:

• PhD
• 2+ years of experience in open source programming languages for large-scale data analysis
• 2+ years of experience with machine learning
• 2+ years of experience with relational databases

2+ years of experience with SQL