Data Scientist Lead (Market Risk Quant)
Remote
Information
As a dedicated Data Scientist Lead-Market Risk Quant, you may work from one of our regional offices: San Antonio, TX, Plano, TX, Phoenix, AZ, Tampa, FL, Colorado Springs, CO, Charlotte, NC or work remotely in the continental U.S. with occasional Business travel.
This position will report to the Director - Quantitative Risk Management, Enterprise Market Risk within the second line of defense ERM team. In this role, the Lead Data Scientist will build predictive machine learning (ML) models which will be used to challenge first line capital market assumptions (CMAs) that drive strategic asset allocation decisions. Typical responsibilities will include data gathering / wrangling / exploratory data analysis, feature extraction / engineering, fitting the model to an appropriate ML algorithm, and evaluating model performance. Additionally, this role will be responsible for documentation requirements to aid in internal model validation. The initial focus will be on forecasting interest rates and equity market returns, but that scope may broaden over time.
Company
USAA
Requirements
What you have:
Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative subject area; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree (12 years total experience).
8 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field and 6 years of experience in predictive analytics or data analysis.
6 years proven experience in training and validating machine learning, statistical, physical, and other advanced analytics models.
4 years of demonstrated ability in one or more dynamic scripted language (such as Python) for performing statistical analyses and/or building and scoring AI/ML models.
Experienced ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, etc.
Excellent demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Validated ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Project management experience that demonstrates the ability to anticipate and appropriately handle project achievements, risks, and impediments.
Expert level experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic models, discriminant analysis, support vector machines, decision trees, forest models, etc.
Advanced level of experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors’ algorithms, DBSCAN, etc.
Demonstrated experience in guiding and mentoring junior technical staff in business interactions and model building.
Proven ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.
A consistent track record of communicating results, insights, and technical solutions to Senior Executive Management (or equivalent).
Extensive technical skills, consulting experience, and business savvy to collaborate with all levels and subject areas within the organization.