Principal Associate, Data Scientist - SBB Fraud

Description

The company started in 1988 by using statistical modeling and relational databases to personalize every credit card offer, and has since grown into a Fortune 200 company focused on data-driven decisions.

The Small Business Bank (SBB) Fraud Data Science team builds machine learning models that detect fraud in transactions, account openings and account takeovers, protecting customers and the company. Small businesses make up 99% of US businesses and account for 44% of the country's GDP.

The team works with analysts, software engineers and product managers to build, deploy and manage fraud models that make intelligent decisions.

The role is a Principal Associate, Data Science position, and the company is the company.

Responsibilities

• Work with a cross-functional team of data scientists, software engineers and product managers to deliver a product customers value.
• Use a broad technology stack, including Python, Conda, AWS, H2O and Spark, to find insights in large volumes of numeric and textual data.
• Build machine learning models across every stage, from design through training, evaluation, validation and implementation.
• Translate the complexity of your work into clear business goals for others.

Requirements

• Statistical rigor: you have built, validated and backtested models, can read a confusion matrix or ROC curve, and have experience with clustering, classification, sentiment analysis, time series and deep learning.
• Data handling: you can retrieve, combine and analyze data from many sources and formats, and you understand that knowing the data is often the key to good data science.
• Customer focus: you enjoy analysis and building things, and you make decisions that are right for customers.
• Technical skill: you are comfortable with open-source languages, keep developing them, and have hands-on experience building data science solutions with open-source tools and cloud platforms.
• Basic qualifications: you hold, or are working toward with the degree due on or before the start date, one of the following: a Bachelor's degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science or a related field) plus 5 years of data analytics experience; a Master's degree in a quantitative field, or an MBA with a quantitative concentration, plus 3 years of data analytics experience; or a PhD in a quantitative field (same list).
• 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 3 years of machine learning experience.
• Preferred: at least 1 year of deep learning or emerging AI experience, such as graphs, embeddings, sequence models or transformer models.
• Preferred: at least 3 years of experience with Python, Dask, Spark or similar tools.
• Preferred: at least 3 years of SQL experience.
• Preferred: at least 1 year of experience working with AWS.

Conditions

Location: McLean, VA. Full-time annual base salary is $161,800 to $184,600. Candidates hired in other locations get the range for that location, and the exact offer appears in the offer letter.

Salary for part-time roles is prorated to the agreed hours.

The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI). Incentives may be discretionary or non-discretionary depending on the plan.

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

Applications will be accepted for at least 5 business days.

No agencies. The company is an equal opportunity employer.

Benefits

• Comprehensive health, financial and other benefits. Eligibility depends on full- or part-time status, exempt or non-exempt status and management level.
• Performance-based incentive compensation, as described in conditions.