Data Visualization makes data tangible for our company and customers, thereby empowering us all by transforming what’s unknown into something that’s known. We enable these outcomes by digging into data sets, large and small, to manifest it in enlightening, understandable ways to fuel decision-making.
We’re looking for engineers to work with our Center for Machine Learning to help make their findings tangible, help communicate their work visually, and create impact across the enterprise. Machine Learning touches all aspects of our company, from operations to risk to doing right by our customers.
WHY WE CARE
- Because we’re aiming to Change Banking for Good, which means being brave enough to change how people interact with us AND their money, for the better
- Because we want to know things that’ll enable us to make strategic decisions with the greatest degree of confidence, driven by real-time data
- Because insights should be accessible to us, both as employees of Capital One, and as customers who are trying to make sense of their money
WHO ARE YOU? LIKELY SOMEONE WHO…
- Is deeply creative and driven by playfulness, wonder, and a thirst for understanding
- A problem solver with a knack for uncovering elegant solutions
- Is a confident person who’s humble and never pretends to be someone you’re not
- Enjoys fully immersing yourself in the data sets, business constraints, and customer problems you’re interpreting.
- Asks questions to get at the deeper-level “why” because you prioritize relevance and meaning over the artifact or visualization itself
- Is outcome-focused and have an exceptional track record to prove it
- Is fascinated by AI, ML, and Deep Learning. You might not build anything in this space, but you can hold your own in a conversation and delve into nuanced discussion.
HERE’S WHAT YOU CAN EXPECT IN THIS ROLE
As a Capital One Data Visualization Engineer with an ML focus, you may work on everything from internal tools and platforms, libraries, and communication visualizations. Here’s what you’ll be doing:
Seek: You will sit with ML team and product owners to discuss their problem space. Rather than take orders on what to build, you’ll engage users in conversations to uncover how they do their work, what they need to accomplish, how they understand their work, and how they currently solve their problems. You will work to explore pain points and areas for meaningful improvement.
Assess: For a given problem space, you will assess whether data visualization is an appropriate solution and to what degree. You recognize that data visualization isn’t the only tool in the toolbox and will advise if the project isn’t a good candidate for the team’s expertise.
Design: You’ll spend the majority of your time finding the shape of data. This entails immersing yourself in the data and exploring the data using tools such as Tableau, Processing, R, D3, RAW, Gephi, and others. You’ll rabidly pull apart and tease out the nuance of the data to understand the breadth, depth, extents, outliers, relationships, and other factors that reveal its structure as a whole. Your sketchbook is your constant companion. It picks up where your tools fail you. Because your mind is finely tuned to envisioning the form of the data, you’ll find most of your solutions come to life on paper.
Collaborate: You’ll work closely with Data Scientists, ML Engineers, UI/UX and Visual Designers. These roles will sometimes overlap a bit, and you’re comfortable adjusting your expectations to facilitate the group effort.
Improve Insatiably: You will continuously find and assess emerging technologies, libraries, and tools. You strive to improve your work and workflow and constantly look for tools and processes that will make you more efficient, nimble, and help bring to life your nuanced solutions.
Learn: Passion for your craft drives you to stay on top of the latest developments in the field. You’re comfortable reaching out to experts and your peers alike to facilitate your understanding of techniques and tools.
Share: You’ll look for opportunities to create open source libraries, tools, and frameworks. You’ll build a culture of sharing in your team. Your participation in external and internal communities alike is expected and encouraged.
Let’s talk – everything you’ve just read is intended to be a jumping-off point for our conversation. So if you’re intrigued and would like to learn more, please email email@example.com about what’s particularly interesting to you about this opportunity.