Senior Marketing Data Scientist

USAA•Tampa, FL

22h•Hybrid

About The Position

The USAA Marketing Strategy & Analytics (MS&A) team is ensuring every member we acquire is a family whose financial security we help build. Our team is committed to providing granular and actionable visibility into our marketing acquisition investments. We are looking for a Senior Marketing Data Scientist to join the team. You will lead the technical work behind audience strategy: building out and evaluating models, integrating 1st and 3rd party data, and resolving identities across marketing channels. Your work will directly shape which audiences we pursue and how we optimize our investments toward them. As a senior individual contributor, you will set technical standards, mentor other analysts, and partner closely with our MarTech, engineering and marketing leads on marketing activation. This role is remote eligible in the continental U.S. with occasional business travel. However, individuals residing within a 60-mile radius of a USAA office will be expected to work on-site 4 days per week. Relocation assistance is available for this position.

Requirements

Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; 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.
6 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 discipline and 4 years of experience in predictive analytics or data analysis.
4 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
4 years of experience in one or more dynamic scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
Proven experience writing 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, images, etc.
Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
Experience guiding and mentoring junior technical staff in business interactions and model building.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

Nice To Haves

Prior U.S. military service or being a military spouse/domestic partner is highly valued.
3+ years in marketing analytics audience strategy, or paid media.
3+ years working with 3rd party data sources (data co-ops, data marketplaces, credit bureaus) to build marketing targeting models.
3+ years in digital platforms (Adobe, Google) and clickstream data.
3+ years in identity resolution - deterministic/probabilistic matching, household graphs, clean rooms, etc.
Expert proficiency in SQL and Python or R; comfortable in cloud data environments (Snowflake, Bigquery, Databricks).
Experience with data visualization or BI tools (Tableau, Power BI) to communicate insights effectively.

Responsibilities

Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences.
Assesses business needs to propose/recommend analytical and modeling projects to add business value.
Works with business and analytics leaders to prioritize analytics and modeling problems/research efforts.
Builds and maintains a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
Translates complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
Manages project milestones, risks, and impediments.
Escalates potential issues that could limit project success or implementation.
Develops best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
Maintains expertise and awareness of cutting-edge techniques.
Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
Serves as a mentor to junior data scientists in modeling, analytics, and computer science tasks.
Participates in internal communities that drive the maintenance and transformation of data science technologies and culture.
Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.