Principal Associate, Data Science - US Card (Upmarket Segments)

Description

The company started as a small business that changed credit card offers by personalizing each one with statistical models and relational databases, back in 1988. Over the following years that approach grew it into a Fortune 200 company and a leader in data-driven decisions.

This is a Principal Associate, Data Science role on the US Card (Upmarket Segments) side, as a data scientist on a team pushing that work to a much larger scale with current computing and machine learning tools. The team works across billions of customer records to find chances that help ordinary people save money, time and stress with their finances.

Responsibilities

• Work with data scientists, software engineers and product managers to ship a product that customers value.
• Use a wide set of tools, including Python, Conda, AWS, H2O and Spark, to uncover patterns in large amounts of numeric and text data.
• Build machine learning models at every stage, from design and training through evaluation, validation and deployment.
• Explain complex analysis in terms of concrete business goals for non-technical colleagues.

Requirements

• Put customers first and make decisions that are right for them.
• Keep up with research and new methods, and look for ways to apply them.
• Frame large, unclear problems, ask probing questions, and propose new ideas.
• Challenge standard thinking, work with stakeholders to improve current processes, and grow talent on your own team and beyond.
• Work comfortably with open-source languages and have hands-on experience with open-source tools and cloud platforms.
• Have built, validated and backtested models, and can read a confusion matrix or ROC curve. Experience with clustering, classification, sentiment analysis, time series and deep learning is expected.
• Can pull together and analyze data from many sources and formats, and see that understanding the data comes first.
• Basic qualifications, with the degree either already held or expected by the scheduled start date: a bachelor's degree in a quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science or a related field, plus 5 years of data analytics experience; or a master's degree in one of those fields, or an MBA with a quantitative concentration, plus 3 years of data analytics experience; or a PhD in one of those fields.
• Preferred: a master's degree in a STEM field (Science, Technology, Engineering or Mathematics) plus 3 years of data analytics experience, or a PhD in a STEM field.
• Preferred: at least 1 year working with AWS.
• Preferred: at least 3 years of experience in Python, Scala or R.
• Preferred: at least 3 years of machine learning experience.
• Preferred: at least 3 years of SQL experience.

Conditions

The company will consider sponsoring a new qualified applicant for work authorization for this position.

Full-time annual base salary ranges by location: McLean, VA $161,800 to $184,600; New York, NY $176,500 to $201,400; Richmond, VA $147,100 to $167,900. These ranges apply to hires working in those locations and reflect what the company is prepared to pay at the time of posting.

Candidates hired in other locations will receive the range for their location, and the exact salary will appear in the offer letter.

Part-time pay is prorated by the agreed number of regular hours.

Applications will be accepted for at least 5 business days.

No agencies, please.

Equal opportunity employer, including disability and veteran status, that is drug-free and considers applicants with criminal histories as applicable law allows.

Positions posted in Canada, the United Kingdom or the Philippines are filled by separate local entities of the company.

Benefits

• Eligible for performance-based incentive pay, which may include cash bonuses and or long-term incentives, either discretionary or non-discretionary depending on the plan.
• Access to a broad set of health, financial and other benefits. Eligibility depends on full or part-time status, exempt or non-exempt classification and management level.