Data Scientist II (Mid Level) - Pricing and Product Analytics

USAA Life Insurance Company; United States; Hybrid; USD 90,000 - 130,000; Full time

Job summary

USAA Life Insurance Company is seeking a Data Scientist II (Mid Level) for Pricing and Product Analytics on Auto and Property teams. Remote-eligible with occasional travel; on-site four days a week within 60 miles of USAA offices.

You will translate business problems into statistical, ML, and optimization solutions to inform insights and drive value, collaborating with engineering and data science peers to scale models and validate results before production.

Qualifications

• Bachelor's degree in a quantitative field or 4 years of relevant education/experience.
• 2 years of experience in predictive analytics or data analysis (or Advanced Degree).
• Experience in training and validating statistical, physical, ML, and other analytics models.
• Experience in Python or R for statistical analyses and/or building and scoring AI/ML models.
• Ability to write clear, well-documented code.
• Experience querying and preprocessing data using SQL/NoSQL/HQL.
• Experience handling structured, semi-structured, and unstructured data files.
• Knowledge of supervised modeling (regression, SVM, trees, etc.).
• Knowledge of unsupervised modeling (clustering, DBSCAN, etc.).
• Ability to communicate analytical results to non-technical partners.

Responsibilities

• Translate business problems into statistical, ML, simulation, and optimization solutions.
• Collaborate with engineering to deliver scalable solutions and customer-facing applications.
• Leverage databases, cloud, and programming to build analytic models.
• Collaborate with data scientists to improve tooling and model risk management.
• Validate results and ensure stability before production.

Skills

Statistical Modeling

Data Analysis

Communication of results

Problem Solving

Education

Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or quantitative field

4 years of relevant education and/or experience

Tools

Python

R

SQL

Job description

Why USAA?

At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families.

Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful.

We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.

The Opportunity

We are seeking a dedicated Data Scientist II (Mid Level) - Pricing and Product Analytics.

We have multiple remote-eligible opportunities available on our Auto and Property Data Science teams that build loss, acquisition, retention, and lifetime value models.

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 four days per week.

What you'll do:

As a Data Scientist, you will translate business problems into applied statistical, machine learning, simulation, and optimization solutions to inform actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction. In collaboration with engineering partners, you will deliver solutions at scale, and enable customer-facing applications. You will leverage database, cloud, and programming knowledge to build analytical modeling solutions using statistical and machine learning techniques. You will collaborate with other data scientists to improve USAA's tooling, expanding the company's library of internal packages and applications and work with model risk management to validate the results and stability of models before being pushed to production at scale.

• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• 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 technical documents for knowledge persistence, risk management, and technical review audiences. Consults with peers for guidance, as needed.
• Translates business request(s) into specific analytical questions, executing on the analysis and/or modeling, and communicating outcomes to non-technical business colleagues.
• Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions that are aligned with the customer's vision and specifications and consistent with modeling best practices and model risk management standards.
• Seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

What you have:

• Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience.
• 2 years of experience in 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.
• Experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 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.
• Ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
• Experience working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• 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.
• Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
• Ability to communicate analytical and modeling results to non-technical business partners.