Role Summary
As a Data Scientist, you will help turn data into decisions by combining strong technical execution with growing business awareness and communication skills. Working alongside more senior data 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.
What You’ll Do
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.
Core Qualifications
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.
Building for the Age of AI
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: comfort using retrieval-augmented generation (RAG), embeddings, and prompting following established patterns.
Building alongside agentic systems: contributing to LLM/agent workflows that someone more senior has architected.
Evaluation basics: helping test model and LLM output against defined quality metrics.
Experimentation fundamentals: understanding the difference between what predicts an outcome and what changes it.
AI-augmented working style: using AI coding assistants to move faster while sanity-checking their output rather than trusting it by default.
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.).
Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.About Vanguard
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.
How We Work
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.
Skills Required
- 3+ years of data science / ML experience
- Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Economics, Analytics, or related quantitative field (or equivalent experience)
- Working proficiency in Python
- Working proficiency in SQL
- Solid foundation in statistical and machine learning methods and model validation
- Exposure to cloud environments (AWS, Azure, or GCP)
- Familiarity with standard tooling such as Git and Jupyter
- Clear communication skills and strong desire to learn
- Graduate degree in a quantitative field
- Experience or curiosity with GenAI, LLMs, RAG, and embeddings
- Experience building or working with agent/LLM workflows and evaluation basics
- Project or coursework experience with recommendation, ranking, or decision-support problems
- Familiarity with notebooks-to-production workflows and version control practices
- Exposure to big-data frameworks (e.g., Spark)
Vanguard Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Vanguard and has not been reviewed or approved by Vanguard.
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Retirement Support — Retirement support appears unusually strong through a 401(k) design that includes a match plus an additional employer contribution, which can materially lift long-term total rewards. HSA seeding and an enhanced employer match further strengthen the savings-and-benefits value of the package.
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Wellbeing & Lifestyle Benefits — Wellbeing and lifestyle support is reinforced by a sizable annual FlexFund stipend that can be applied across many day-to-day categories such as fitness, childcare, and other personal expenses. On-site or virtual clinics and fitness options add practical health and wellness convenience.
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Affordable Benefits — Healthcare and related benefits are positioned as comparatively affordable via heavily subsidized medical plans and broad coverage options. This affordability can offset moderate base pay for employees who place higher value on out-of-pocket cost reductions.
Vanguard Insights
What We Do
We are a community of 30 million who think – and feel – differently about investing. Together, we’re changing the way the world invests. Since our founding in 1975, helping our investors achieve their goals is our sole reason for existence. With no other parties to answer to and therefore no conflicting loyalties, we make every decision—like keeping investing costs as low as possible—with only your needs in mind. Vanguard is one of the world's largest investment companies, offering a large selection of high-quality low-cost mutual funds, ETFs, advice, and related services. Individual and institutional investors, financial professionals, and plan sponsors can benefit from the size, stability, and experience Vanguard offers. As of April 30, 2019, we managed more than $5.6 trillion in global assets. In addition, we have 189 funds in the United States and 225 funds in global markets. For Commenting Guidelines & Important information, visit here: http://vanguard.com/linkedin Vanguard Marketing Corporation, Distributor.
