Director, Data Scientist - US Card

Description

The company started as a credit card business that personalised every offer with statistical modelling and relational databases. Since then it has grown into a large financial company that relies heavily on data for decisions.

You will join a team using current computing and machine learning tools on billions of customer records to build products that help everyday people manage money more easily.

The US Card Data Science group uses customer data to shape business strategy and improve customer experience. The team builds and deploys data science and machine learning models for the whole credit card lifecycle, including marketing, acquisition, underwriting and fraud prevention. The work affects financial products used by millions of cardholders every day.

Responsibilities

• Work with data scientists, software engineers and product managers to deliver a product customers want to use.
• Use a wide technology stack, including Python, Conda, AWS, H2O and Spark, to find patterns in large volumes of numeric and text data.
• Build machine learning models from design through training, evaluation, validation and deployment.
• Explain technical work clearly enough to turn it into business goals.

Requirements

• Put customers first and make decisions that are right for them.
• Research new technologies and methods, and look for ways to apply them.
• Take on large, loosely defined problems, ask questions and push for answers, and share new ideas.
• Challenge existing thinking, work with stakeholders to improve current approaches, and help develop talent on your team and beyond.
• Be comfortable with open-source languages and keep building your skills in them. Have hands-on experience building data science solutions with open-source tools and cloud platforms.
• Build, validate and backtest models. Understand confusion matrices and ROC curves. Have experience with clustering, classification, sentiment analysis, time series and deep learning.
• Handle large datasets and retrieve, combine and analyse data from varied sources and formats. Understand that knowing the data is often the key to good data science.
• You must currently hold, or be on track to earn before the start date, one of the following: a Bachelor's degree in a quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science or a related field, plus 9 years of data analytics experience; a Master's degree in a quantitative field or an MBA with a quantitative concentration, plus 7 years of data analytics experience; or a PhD in a quantitative field, plus 4 years of data analytics experience.
• At least 4 years of experience using open-source programming languages for large-scale data analysis.
• At least 4 years of experience with machine learning.
• At least 4 years of experience with relational databases.
• Preferred: a PhD in a STEM field (Science, Technology, Engineering or Mathematics) plus 5 years of data analytics experience.
• Preferred: at least 1 year of experience with AWS.
• Preferred: at least 3 years of experience managing people.
• Preferred: at least 5 years of experience in Python, Scala or R for large-scale data analysis.
• Preferred: at least 5 years of experience with machine learning.
• The company will consider sponsoring a new qualified applicant for work authorisation for this position.

Conditions

Base salary ranges for full-time roles: Cambridge, MA and McLean, VA $269,100 to $307,200; New York, NY $293,600 to $335,100. Part-time pay is prorated to the agreed hours.

Pay for other locations follows that location's range. The exact salary is stated in the offer letter.

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

The application window will stay open for at least 5 business days.

No agencies, please. The company is an equal opportunity employer and does not discriminate on protected grounds, and it considers applicants with criminal records in line with applicable law.

Benefits

• A broad package of health, financial and other benefits. Eligibility depends on full- or part-time status, exempt or non-exempt status and management level.