Role Summary

Reporting to the Manager of Data Analytics, Insights & Measurement, theAnalytics EngineeringData Analyst will help transform complex business needs into scalable analytical products and durable capabilities for T. Rowe Price's Individual Investors (II) business. II is a dynamic and rapidly evolvingcomponentof the firm's growth, focused on empowering individual clients through innovative products, digital experiences, and personalized service. The Analytics Engineering program extends beyond one-time analysis by building repeatable solutions that improve how the organization measures, predicts, automates, and acts on data.

In this role, you will combine advanced analytics withastrong engineering discipline. You will design and develop analytical layers, models, reusable data products, automation, and measurement solutions; apply advanced statistical and machine-learning methods to complex questions; and improve or refactor existing solutions. You will partner with stakeholders and other analytics teams on strategic initiatives, break-fix needs, surge support, and opportunities where advanced methods can create sustained value.

This is an excellent opportunity for an analyst who enjoys building - not just answering a question oncebut creatingthe capability to answer it repeatedly and better over time. You will join a collaborative environment that values innovation, technical rigor, documentation, experimentation, and measurable business impact. The strongest candidates will be comfortable moving between analysis and engineering, translating ambiguity into well-designed solutions, andproductizinghigh-value ideas so they can scale across the enterprise.

Responsibilities

Build scalable analytical solutions and products:

• Design and develop medium- to complex analytical products, reusable components, and scalable solutions that address important business needs.
• Translate business requirements into clear solution designs, data models, metrics, features, and technical components.
• Develop successful analyses and prototypes so they can bemaintained, reused, and scaled across teams and use cases.

Advance modeling, forecasting, and applied analytics:

• Perform advanced modeling and statistical analysis on mature data and products to solve complex problems and deliver deeper insight.
• Apply methods such as forecasting/time series, experimental design and A/B testing, segmentation, machine learning, predictive analytics, prescriptive analytics, optimization, and simulation whereappropriate.
• Evaluate model and solution performance,establishmeasurement approaches, and iterate based on results and business outcomes.

Build and improve the analytics layer:

• Design analytics-layer datasets, semantic structures, features, metrics, and data products that make downstream analysis more reliable and efficient.
• Use advanced SQL and Python to transform data, automate repeatable processes, reduce manual work, and improve solution reliability.
• Partner with the Data Management team to apply data engineering fundamentals and cloud warehouse practices to work effectively with large datasets and modern analytical environments.

Innovate, automate, andoptimize:

• Identifyopportunities to automate recurring work, expand GenAI-enabled workflows, and simplify or refactor existing solutions.
• Improve solution performance, transparency, maintainability, and efficiency through sound architecture and coding practices.
• Introduce practical technical improvements that increase analytical throughput and create durable enterprise capability.

Partner on strategic initiatives and advanced methods

• Support high-impact strategic initiatives and analytical transformation efforts with advanced methods and strong technical consultation.
• Partner with Rapid Insights and business teams when urgent needs require advanced analyticalsupport, andhelpdeterminewhen a quick-turn solution should evolve into a durable product.
• Communicate technical findings, design choices, risks, and trade-offs clearly to both technical and nontechnical stakeholders.

Apply engineering discipline and quality standards:

• Use Git/version control, testing, peer review, documentation, and change-control practices to ensure solutions are reliable, reproducible, and supportable.
• Work within Agile delivery practices, manage work through tools such as Jira or Planner, and contribute to transparent backlogs, iteration, and continuous improvement.
• Use AI and Copilot capabilities whereappropriate toaccelerate development, documentation, testing, and quality assurance whilemaintainingcontrols and accuracy.

Qualifications

Required:

• Bachelor's degree in Data Science, Operations Management, Operations Research,Data Analytics,Statistics, Computer Science, Information Systems, or related degree/field.
• 3+ years of total relevant work experience.
• Proficiencywith writingcomplexSQL queries.
• Proficiencywith Python, including experience with machine-learning packages.
• Experience with visualization software such as Tableau or Power BI to communicate business performance and status.
• Proficient in analytical thinking, applied statistics, SQL, Python/R, Snowflake or comparable cloud data warehouses, data modeling, automation, Git/version control, Agile development, testing and quality assurance, performance optimization, and documentation.
• Proficient in data storytelling, executive communication, requirements gathering, AI/Copilotutilization, and collaborative work across technical and business teams.

Preferred:

• Master's degree in Data Science or Computer Science
• Experience with Snowflake orJupyterHub.
• Experience withAnyLogicor other simulation-based software or programming.
• Experience with ARIMA, Prophet, and other time-series forecasting methods.
• Experience with linear programming and solving optimization-based problems with mathematical solvers.
• Ability to develop and communicate analytical results and insights for partners at all levels within the organization, including senior management.
• Strong interpersonal skills, including the ability to build trust and confidence, strengthen relationships through ongoing dialogue and interactions, and collaborate effectively with others.

FINRA Requirements

FINRA licenses are not required and will not be supported for this role.

Work Flexibility

This role is eligible for hybrid work, with up to one day per week from home.

Base Salary Ranges

Please review the job posting for the location of this specific opportunity.

Placement within the range provided above is based on the individuals relevant experience and skills for the role. Base salary is only one component of our total compensation package. Employees may be eligible for a discretionary bonus, which is determined upon company and individual performance.

This job posting is expected to be available until:

Benefits