Manager, Data Scientist - Financial Services - Pricing

Description

The company built its early business on personalizing every credit card offer with statistical modeling and relational databases, and has since grown into a large financial services firm focused on data-driven decisions.

The team is working on the next wave of change at much larger scale, using current computing and machine learning tools across billions of customer records to find opportunities that help everyday people save money, time and stress in their finances.

Responsibilities

• Work with data scientists, software engineers and product managers to build a product that customers value.
• Use a wide range of tools, including Python, Conda, AWS, H2O and Spark, to find insights in large volumes of numeric and text data.
• Build machine learning models across every stage, from design and training through evaluation, validation and deployment.
• Explain the technical substance of your work in terms of concrete business goals for non-technical stakeholders.

Requirements

• Customer first: you analyze and build with care, and you make decisions that serve customers.
• Innovative: you keep up with new research and tools and look for ways to apply them.
• Creative: you enjoy turning large, vague problems into clear ones, asking questions and proposing new ideas.
• Leadership: you question conventional thinking, work with stakeholders to improve existing approaches, and invest in developing people on your team and beyond.
• Technical: you are comfortable with open-source languages, keep improving them, and have hands-on experience building data science solutions with open-source tools and cloud platforms.
• Statistically minded: 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-savvy: you can retrieve, combine and analyze data from many sources and formats, and you know that understanding the data usually leads to good data science.
• Required: a bachelor's degree in a quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics or Computer Science, or a related field, plus 6 years of data analytics experience; or a master's degree in one of those fields, or an MBA with a quantitative concentration, plus 4 years of data analytics experience; or a PhD in one of those fields plus 1 year of data analytics experience. You may be near completion, as long as the degree will be finished on or before the scheduled start date.
• Required: at least 1 year using open-source programming languages for large-scale data analysis.
• Required: at least 1 year of machine learning experience.
• Required: at least 1 year using relational databases.
• Preferred: a PhD in a STEM field (Science, Technology, Engineering or Mathematics) plus 3 years of data analytics experience.
• Preferred: at least 1 year working with AWS.
• Preferred: at least 4 years in Python, Scala or R for large-scale data analysis.
• Preferred: at least 4 years of machine learning experience.
• Preferred: at least 4 years of SQL experience.

Conditions

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

Full-time annual salary: McLean, VA $197,300 to $225,100 and Plano, TX $179,400 to $204,700, both for the Manager, Data Science title. This refers only to roles based in these locations, and is the amount the company will pay at the time of posting. Part-time salaries are prorated to the agreed hours. Candidates hired in other locations get the range for that location, and the exact offer is stated in the offer letter.

Applications will be accepted for at least 5 business days.

No agencies, please. The company is an equal opportunity employer that does not discriminate, including on the basis of disability or veteran status, and it promotes a drug-free workplace. Applicants with criminal histories are considered in line with applicable laws.

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