Consumer Product Strategy Analyst III (Analytics & Model Development)

📍 Job Overview

Job Title: Consumer Product Strategy Analyst III (Analytics & Model Development)

Company: Bank of America

Location: Charlotte, NC (with potential for other listed locations: Rio Rancho, NM; Plano, TX; Fort Worth, TX; Las Vegas, NV; Scranton, PA; Jacksonville, FL; Newark, NJ; Hunt Valley, MD; Richmond, VA; Phoenix, AZ; Utica, NY; Tampa, FL)

Job Type: Full-time

Category: Analytics & Strategy Operations / Data Science

Date Posted: 2026-07-30

Experience Level: Mid-Level (2-5 years)

Remote Status: Hybrid (In-office expectations with flexibility)

🚀 Role Summary

• This role is central to developing and implementing sophisticated analytical models and strategies within consumer product lines, focusing on minimizing financial loss and enhancing customer experience.

• It requires a strong foundation in data science, statistical modeling, and advanced analytics to identify trends, predict risks, and recommend data-driven operational improvements.

• The position involves extensive use of data manipulation and visualization tools to source, validate, transform, and integrate data from various operational platforms.

• A key aspect of the role is collaborating with cross-functional teams to translate complex data insights into actionable strategies and scalable reporting solutions that inform critical business decisions.

📝 Enhancement Note: While the title is "Consumer Product Strategy Analyst," the detailed responsibilities and required skills heavily lean into data analytics, modeling, and operations within a financial services context. This position is ideal for individuals with a strong quantitative background who are looking to apply their analytical prowess to strategic product and risk management challenges in a large financial institution. The focus on "loss exposure" and "fraud risk" suggests a strong connection to risk operations and GTM enablement for risk mitigation strategies.

📈 Primary Responsibilities

• Leverage data science techniques, including statistical methods and AI capabilities, to identify key business trends, diagnose root causes of issues, and uncover actionable insights for operational enhancement.

• Design, develop, and maintain robust ETL workflows, automated reports, data quality checks, and recurring data distributions to support strategic business goals and operational decision-making.

• Apply advanced analytical tools such as SQL, Python, SAS, and Tableau to perform complex data analysis, build predictive models, support forecasting optimization, and generate prescriptive insights for risk mitigation and product strategy.

• Source, validate, transform, and integrate diverse datasets from critical platforms including workforce management, contact center operations, vendor performance, capacity planning, and overall business performance metrics.

• Lead collaborative reporting and design sessions with organizational stakeholders and business owners to define requirements, build, and deliver scalable and impactful reporting solutions that align with business objectives.

• Actively participate in Agile standup meetings, providing timely status updates, supporting User Acceptance Testing (UAT) and deployment readiness, and proactively communicating risks, blockers, and dependencies.

• Collaborate with fellow developers and data professionals to establish and maintain scalable processes, reusable logic, comprehensive documentation, robust governance standards, and best practices in development.

• Partner closely with Performance Optimization, Scheduling, Strategic Planning teams, technology partners, and business stakeholders to deliver critical insights that inform strategic decisions and contribute to achieving overarching business goals.

• Manage a portfolio of multiple projects concurrently, adapting to shifting priorities while consistently delivering accurate, timely, and high-quality analytical work products.

• Evaluate data to proactively assess potential fraud risk, develop effective mitigation strategies, and implement policy and procedural changes within segmentation structures to optimize results and minimize negative impacts.

📝 Enhancement Note: The responsibilities clearly indicate a need for deep analytical rigor and a hands-on approach to data manipulation and model development. The emphasis on "fraud risk" and "loss exposure" highlights a critical operations function within the financial services sector. Candidates should be prepared to demonstrate experience in developing and implementing data-driven solutions that directly impact financial outcomes and customer experience.

🎓 Skills & Qualifications

Education:

• Bachelor’s degree in a quantitative discipline such as mathematics, statistics, economics, business, engineering, finance, or operations research is strongly desired. Experience:

• 2-5 years of progressive experience in data analysis, statistical modeling, and analytics within a corporate or financial services environment.

• Proven experience in developing and implementing data-driven strategies and analytical models. Required Skills:

• SQL and/or SAS Coding: 1+ years of experience in writing complex queries and developing analytical routines using SQL and/or SAS (SAS EG / SAS Studio experience is a plus).

• Tableau Proficiency: 1+ years of experience in creating dashboards, visualizations, and reports using Tableau for business intelligence and data storytelling.

• Python for Data Analysis: Demonstrated experience with Python for data manipulation, statistical analysis, and potentially AI/ML applications.

• Advanced Excel Skills: High proficiency in Excel for data analysis, modeling, and reporting, including advanced functions, pivot tables, and data manipulation.

• Communication Skills: Strong written and oral communication abilities, with the capacity to articulate complex technical concepts to both technical and non-technical audiences.

• Project Management: Ability to manage multiple projects simultaneously in a dynamic and complex environment, demonstrating excellent time management and organizational skills.

• Analytical & Problem-Solving: Robust analytical and critical thinking skills to dissect complex problems, identify root causes, and develop effective, data-backed solutions.

• Teamwork & Independence: Proven ability to work effectively both independently and as a collaborative member of a team.

• Positive Attitude: A proactive and willing attitude towards learning new technologies, methodologies, and business domains.

Preferred Skills:

• Advanced Analytical & Quantitative Skills: Demonstrated ability to leverage data and metrics to identify root causes and drive strategic decisions.

• Database Experience: Familiarity with database systems such as SSMS, Oracle, Hadoop, Teradata.

• Agile & SDLC: Understanding of Software Development Life Cycle (SDLC) concepts and experience with project management tools like Agile and Jira.

• R Experience: Additional experience with R for statistical computing and graphics is a plus.

📝 Enhancement Note: The "1+ years" requirement for SQL/SAS and Tableau, coupled with the "2-5 years" overall experience, suggests this role is targeted at individuals who have moved beyond entry-level analyst positions and possess a solid foundational understanding of core analytical tools and techniques within a business context. The preference for a quantitative degree and experience with specific databases like Teradata or Hadoop indicates a need for robust data handling and processing capabilities.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Process Improvement Case Studies: Showcase examples of how you've analyzed existing business processes (e.g., data reporting, workflow automation, risk assessment) and implemented data-driven improvements that led to measurable efficiency gains, cost reductions, or risk mitigation.

• Model Development & Validation: Include examples of analytical models you've developed (e.g., predictive models, forecasting models, segmentation models), detailing the data sources, methodologies, validation techniques, and the business impact achieved.

• Data Integration & ETL Workflows: Present case studies demonstrating your ability to source, clean, transform, and integrate data from disparate systems, highlighting the tools and techniques used to build robust and scalable data pipelines.

• Reporting & Dashboard Design: Provide examples of interactive dashboards and automated reports you've created using tools like Tableau, clearly illustrating how these visualizations communicate complex data insights to various stakeholders and support decision-making.

Process Documentation:

• Workflow Design & Optimization: Evidence of documenting and optimizing operational workflows, particularly those involving data analysis, reporting, or model deployment, to ensure clarity, efficiency, and scalability.

• Data Governance & Quality: Demonstrate an understanding of data quality principles and how you've implemented checks and balances within your analytical processes to ensure data integrity and reliability.

• Measurement & Performance Analysis: Showcase how you've established key performance indicators (KPIs) and conducted ongoing performance analysis for processes or models you've managed, using data to drive continuous improvement.

📝 Enhancement Note: For this role, a portfolio should heavily emphasize quantitative achievements and the practical application of analytical skills. Candidates should be prepared to walk through their projects, detailing the problem, their approach, the tools used, the results obtained, and the lessons learned. Demonstrating an understanding of the full data lifecycle, from sourcing to insight generation and actionable recommendations, is crucial.

💵 Compensation & Benefits

Salary Range: $83,800 - $127,000 annually. Offers will be determined based on experience, education, and skill set.

Benefits:

• Comprehensive Benefits Package: Industry-leading benefits designed to support employee well-being.

• Paid Time Off: Generous provisions for paid time off, allowing for work-life balance.

• Flexible Benefits: Access to flexible benefit options, catering to diverse employee needs.

• Discretionary Incentive: Eligibility to participate in an annual discretionary incentive plan, with awards based on individual performance, business unit contributions, and overall company success.

• Resources & Support: Access to various resources and support systems to foster employee growth and contribution.

Working Hours: 40 hours per week, typically within the 1st shift (standard business hours) in the United States.

📝 Enhancement Note: The salary range provided is competitive for a mid-level analyst role with specialized quantitative skills in a major financial institution. The inclusion of a discretionary incentive plan is common in such roles and highlights the performance-driven nature of the position. The "benefits eligible" status confirms that standard employee benefits will be provided.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services | Banking

Company Size: Bank of America is one of the world's largest financial institutions, employing hundreds of thousands of individuals globally. This large scale offers extensive resources, diverse career paths, and a structured corporate environment.

Founded: Bank of America was formed in 1998 through the merger of NationsBank and BankAmerica, tracing its roots back to 1874. This long history signifies stability, extensive market knowledge, and a deep understanding of financial operations.

Team Structure:

• Operations Focus: The role sits within a broader "Consumer Product Strategy" function, implying a focus on the operational aspects of consumer banking products, including analytics, risk management, and customer experience optimization.

• Analytics & Model Development Specialization: The specific team likely comprises data scientists, analysts, and model developers who collaborate closely to deliver insights and solutions for product management and risk teams.

• Cross-Functional Collaboration: Expect to work with various departments, including product management, risk management, technology, marketing, and potentially compliance, requiring strong communication and stakeholder management skills.

Methodology:

• Data-Driven Decision Making: A core tenet is using data and rigorous analysis to inform strategic decisions, product development, and risk mitigation.

• Agile Development Practices: The mention of Agile standups and Jira suggests an adoption of agile methodologies for project management, emphasizing iterative development, collaboration, and rapid response to business needs.

• Process Optimization: Continuous improvement of operational processes, data workflows, and analytical models is a key driver for efficiency and effectiveness.

Company Website: https://www.bankofamerica.com/

📝 Enhancement Note: Bank of America's culture emphasizes "Responsible Growth," which translates to a focus on ethical operations, customer well-being, and sustainable business practices. For an analyst role, this means understanding the broader impact of analytical work on customers, the company's reputation, and regulatory compliance. The hybrid work model reflects a modern approach that balances in-office collaboration with individual work flexibility.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as an Analyst III, indicating a mid-level position with increasing responsibility beyond entry-level tasks. It requires a strong command of analytical tools and methodologies, the ability to work independently on complex projects, and the capacity to contribute to strategic initiatives.

Reporting Structure: The Analyst III will likely report to an Analytics Manager or Director within the Consumer Product Strategy or a related analytics division. They will collaborate with senior analysts, product managers, risk officers, and technology partners.

Operations Impact:

• Risk Mitigation: Directly contributes to minimizing financial losses by identifying fraud risks, developing mitigation strategies, and refining predictive models.

• Customer Experience Enhancement: Insights derived from analysis can lead to improvements in product offerings, service delivery, and overall customer satisfaction.

• Strategic Decision Support: Provides critical data-backed insights that inform product development, market strategy, and operational efficiency improvements across consumer banking.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific analytical tools (e.g., advanced Python libraries, machine learning frameworks, big data technologies) or modeling techniques.

• Domain Expertise: Develop a comprehensive understanding of consumer banking products, market dynamics, and risk management within the financial services industry.

• Leadership Development: Progress into senior analyst or lead roles, managing projects, mentoring junior analysts, and taking on more strategic responsibilities.

• Cross-Functional Mobility: Opportunities to move into related areas such as product management, risk management, or business intelligence within Bank of America.

📝 Enhancement Note: As an Analyst III, this role is a crucial stepping stone. The emphasis on model development and analytics within a regulated industry like banking provides a strong foundation for a career in data science, quantitative analysis, or risk management. Bank of America's size and diverse business lines offer numerous avenues for long-term career growth and specialization.

🌐 Work Environment

Office Type: Bank of America emphasizes an "in-office culture that supports collaboration, engagement, and career development." While the role is designated as hybrid, there are clear in-office expectations, suggesting a structured approach to in-office days.

Office Location(s): The primary location listed is Charlotte, NC, but the job description also lists numerous alternative locations across the US. This indicates potential for roles to be based in any of these hubs, with specific office requirements likely varying by location. The listed locations include:

• Charlotte, NC

• Rio Rancho, NM

• Plano, TX

• Fort Worth, TX

• Las Vegas, NV

• Scranton, PA

• Jacksonville, FL

• Newark, NJ

• Hunt Valley, MD

• Richmond, VA

• Phoenix, AZ

• Utica, NY

• Tampa, FL Workspace Context:

• Collaborative Spaces: The in-office expectation suggests access to collaborative workspaces designed for team meetings, brainstorming sessions, and cross-functional discussions.

• Technology Infrastructure: As a major financial institution, expect access to robust IT infrastructure, high-performance computing resources, and a wide array of analytical software and tools.

• Team Interaction: Opportunities for direct interaction with colleagues, managers, and stakeholders, fostering a strong sense of team cohesion and shared purpose.

Work Schedule: The standard work schedule is 40 hours per week, typically within the 1st shift (standard business hours). While a hybrid arrangement is indicated, specific in-office days and flexibility will be guided by the "workplace excellence policy" and role-specific responsibilities.

📝 Enhancement Note: The hybrid model at Bank of America is framed as a balance, prioritizing in-office presence for collaboration while allowing for flexibility. Candidates should be prepared for a structured hybrid environment, understanding that specific requirements for in-office days might be determined by team needs and management. The diverse list of potential locations suggests that the hiring process might be centralized but the role could be filled across multiple company sites.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will likely review applications and conduct an initial screening call to assess basic qualifications, experience, and interest.

• Technical Assessment/Skills Test: Expect a technical evaluation to gauge proficiency in required tools like SQL, SAS, Python, or Tableau. This could be a coding challenge or a case study.

• Hiring Manager Interview: A more in-depth interview focusing on your experience, problem-solving approach, and how you've handled specific analytical challenges.

• Panel/Team Interviews: Interviews with potential team members and stakeholders to assess cultural fit, collaboration style, and ability to work within an operations-focused team.

• Case Study Presentation: You may be asked to present a past project from your portfolio or a hypothetical scenario, demonstrating your analytical process, insights, and communication skills.

Portfolio Review Tips:

• Quantify Achievements: For each project, clearly state the problem, your solution, the tools used, and most importantly, the quantifiable results (e.g., percentage reduction in fraud, uplift in conversion rate, efficiency gain).

• Showcase Tool Proficiency: Highlight projects that demonstrate your mastery of SQL, SAS, Python, Tableau, and Excel. Explain why you chose specific tools for certain tasks.

• Structure for Operations: Frame your case studies around operational challenges and how your analytical solutions improved processes, reduced risk, or enhanced performance.

• Tailor to the Role: Emphasize projects related to fraud risk, loss mitigation, customer experience analytics, or predictive modeling, as these align directly with the job description's core responsibilities.

• Prepare for Deep Dives: Be ready to discuss the nuances of your projects, including data limitations, assumptions made, and alternative approaches you considered.

Challenge Preparation:

• SQL & SAS Proficiency: Practice writing complex queries, performing data transformations, and developing analytical scripts. Be prepared for questions on database concepts and data structures.

• Python & Statistical Methods: Review common Python libraries for data science (Pandas, NumPy, SciPy, Scikit-learn) and fundamental statistical concepts.

• Tableau Dashboard Design: Understand how to create effective visualizations that tell a clear story and support decision-making. Be ready to discuss design principles.

• Problem-Solving Scenarios: Prepare for questions that require you to break down a business problem, identify necessary data, outline an analytical approach, and interpret potential results.

📝 Enhancement Note: The interview process for an analytical role at a large financial institution like Bank of America will be rigorous. A well-curated portfolio that clearly demonstrates quantitative skills, operational impact, and tool proficiency is essential. Expect to articulate your thought process clearly and defend your analytical methodologies.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Essential for data extraction, manipulation, and analysis from relational databases. Proficiency in writing complex queries, joins, subqueries, and window functions is expected.

• SAS: A powerful statistical software suite widely used in finance for data management, advanced analytics, and reporting. Experience with SAS EG or SAS Studio is a strong plus.

• Python: Increasingly critical for data science, automation, and advanced modeling. Expect to use libraries like Pandas, NumPy, SciPy, and potentially Scikit-learn.

• Tableau: The primary tool for data visualization and business intelligence, used to create interactive dashboards and reports for stakeholder communication.

• Advanced Excel: Remains a key tool for ad-hoc analysis, quick modeling, and reporting, requiring proficiency in advanced functions, pivot tables, and data analysis add-ins.

Analytics & Reporting:

• Data Warehousing/Databases: Experience with systems like Teradata, Oracle, or Hadoop is preferred, indicating familiarity with large-scale data environments.

• Business Intelligence Platforms: Beyond Tableau, understanding of BI principles and potentially other platforms is beneficial.

• Statistical Software: Proficiency in statistical methods and their application using