Senior Associate, Data Science - Machine Learning & Engineering

Key Skills

AWS, Azure, Conda, Design of Experiment, Google Products, H2O, Java Programming, Python Programming, R Programming, Scala Programming, SPARK Programming, SQL

Job Description

As a Data Scientist at Capital One's Auto Finance business, you’ll be part of a high performing modeling, analytics, product development team that’s leading the next wave of disruption at a whole new scale. Our team has a relentless focus on the craft of modeling and innovation, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives. You will utilize your strong technical skills to scale machine learning models and products into real-time through designing, building complex data pipelines, and deploying models in our leading-edge cloud-based technology platforms. You will also have the opportunity to develop open-source packages and tools for our community and outside Capital One.

Team Description:

Capital One's Auto Finance (COAF) business has been growing massively year over year on the heels of a robust strategic agenda advancing our technological capabilities to bring the best possible experience to our customers. We face the unique challenge of building products to directly serve dealerships and consumers, while also building robust modeling systems to manage our lending risks. The Data Science team in COAF is driving every stage of business strategy and product development, ranging from marketing and personalization, underwriting, pricing, valuation, loss forecasting, image recognition, text analytics and more.

Role Description :

• In this role, you will:
• You will be part of a cross-functional team of data scientists, software engineers, and product managers to deliver real-time data science products customers love
• Leverage a broad stack of technologies — Python, Tensorflow, AWS, Spark, and more to stay up-to-date on the latest cutting-edge technologies — to create reusable and scalable data pipelines and modeling products
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals
• The Ideal Candidate is:
• Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
• You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
• A data guru. “Big data” doesn’t phase you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.
• Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
• You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Basic Qualifications:

• Bachelor’s Degree plus 2 years of experience in data analytics, or Master’s Degree, or PhD
• At least 1 year of experience in open source programming languages for large scale data analysis
• At least 1 year of experience with machine learning
• At least 1 year of experience with relational databases

Preferred Qualifications:

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics), or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
• At least 2 years’ experience with
• Designing and tuning machine learning models
• Designing and implementing large and complex data pipelines for ML models
• Deploying models into production, monitoring, and maintaining machine learning models
• At least 2 years' utilizing cloud-based solutions (AWS, Azure, Google Cloud)
• At least 2 years' experience in Python, Scala, Java or C++
• Experience in ML model architectures such as deep learning, NLP, recommenders, optimization, computer vision, or anomaly detection