Data Analyst, Specialist

Role Summary

In this role, you will help turn data into decisions by combining strong technical execution with growing business awareness and communication skills. Working alongside more senior scientists and cross-functional partners, you will contribute to solving real business problems and learn how analytics connects to outcomes. You’ll own well-defined components — a model, a feature pipeline, an analysis — while developing the ability to understand stakeholder needs, ask the right questions, and explain your work clearly so others can act on it.

The problems will often arrive partially framed; your role is to execute rigorously while building the judgment to connect technical outputs to business value. This is a hands-on, growth-oriented role on cross-functional teams where you’ll build both technical depth and the communication skills needed to become a trusted analytics partner over time.

• Explain your work clearly to technical and non-technical teammates. Communicate methods, results, and limitations so findings are understood, trusted, and usable in decision-making.
• Build well-scoped models and analyses. Develop and validate models on defined problems such as feature engineering, model fitting, calibration, and validation with guidance on approach and standards.
• Wrangle and prepare data. Access, transform, clean, and document large-scale data; identify and diagnose inconsistencies and gaps.
• Contribute to production. Help deploy and monitor models alongside MLE and engineering, learning the discipline of keeping a live model healthy.
• Run experiments others design. Execute designed experiments and analyses correctly and interpret the results.
• Explain your work clearly. Communicate methods, results, and caveats to your team so findings can be trusted and built on.
• Use AI to work faster. Apply AI coding and analysis assistants to accelerate your own work, while learning to evaluate their output critically.
• Learn the practice. Absorb standards and patterns from senior teammates and contribute to a growing, AI-native analytics community.

• 3+ years of data science / ML experience
• Bachelor’s degree in Statistics, Applied Mathematics, Computer Science, Economics, Analytics, or a related quantitative field — or an equivalent combination of training and experience. Grad degree preferred.
• Working proficiency in Python and SQL and comfort wrangling real, messy data.
• Solid foundation in statistical and machine learning methods and an understanding of model validation.
• Exposure to cloud environments (AWS, Azure, or GCP) and standard tooling (e.g., Git, Jupyter).
• Clear communication and a strong desire to learn.

• We expect this role to use modern AI tools fluently and to grow into building with them.
• Strength or genuine curiosity in several of the following is what we’re looking for: working with GenAI/LLMs (RAG, embeddings, prompting); building alongside agentic systems; evaluation basics; experimentation fundamentals; AI-augmented working style.
• Preferred / Nice to Have: project or coursework experience with recommendation, ranking, or decision-support problems; familiarity with notebooks-to-production workflows and version control; exposure to big-data frameworks (Spark, etc.).

Sponsorship Vanguard is not offering visa sponsorship for this position.

At Vanguard, we don't just have a mission—we're on a mission. To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience. Vanguard serves individual investors, institutions, employer-sponsored retirement plans, and financial professionals.

We have a diverse and talented crew with a culture that promotes teamwork, along with an unwavering focus on serving our clients' best interests.