Analyst, Data Analytics

About this role

At T. Rowe Price, we identify and actively invest in opportunities to help people thrive in an evolving world. As a premier global asset management organization with more than 85 years of experience, we provide investment solutions and a broad range of equity, fixed income, and multi-asset capabilities to individuals, advisors, institutions, and retirement plan sponsors. We take an active, independent approach to investing, offering our dynamic perspective and meaningful partnership so our clients can feel more confident.  

We believe doing the right thing for our clients and our associates is good business . With a career at the firm, y ou can expect opportunities to create real impact at work and in your community. Y ou’ll enjoy resources to support your career path, a s well as compensation , benefits , and flexibility to enrich your life. Here, you’ll find a collaborative culture that respect s and valu e s differences and colleagues who share a spirit of generosity .   

Join us for the opportunity to g row and make a difference in ways that matter to you .  

Role Summary Reporting to the Manager of Data Analytics, Insights & Measurement, the Analytics Engineering Data 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 evolving component of 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 with a strong 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 once but creating the 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, and productizing high-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 be maintained, 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 where appropriate.  • Evaluate model and solution performance, establish measurement 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, and optimize: • Identify opportunities 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 analytical support, and help determine when 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 where appropriate to accelerate development, documentation, testing, and quality assurance while maintaining controls 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

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