Data Scientist, Specialist

Responsibilities

• 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

Benefits

Additional Information

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.

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