Data Scientist, Specialist

Role Summary

As a Data Scientist, Specialist, you will pair deep technical expertise with strong business partnership to turn data into decisions that drive measurable outcomes. You donât just build models: you will be working closely with stakeholders across product, operations, and leadership to translate ambiguous business needs into structured analytic approaches.

What You'll Do

• Lead communication and influence decisions. Translate complex findings into clear, actionable narratives tailored to different audiences; align stakeholders on trade-offs, risks, and recommended actions, and ensure insights result in real business decisions.
• Build and validate models end to end. Develop predictive and prescriptive models on large-scale data: from feature engineering and data foraging through model selection, calibration, and validation.
• Make insights actionable, not just accurate. Design how model output is surfaced to the people who use it: the explanation, the context, and the recommended action. Optimize for a human making a good decision, not just for a leaderboard metric.
• Ship to production and keep it healthy. Partner with MLE and engineering to deploy models, then own monitoring for drift, degradation, data quality, and real-world performance against business outcomes.
• Probe the business, then structure the problem. Engage stakeholders to understand processes and drivers, bring structure to ambiguous requests, and translate them into a defensible analytic approach.
• Design and run experiments. Apply sound experimental and causal reasoning to measure impact and to distinguish what predicts an outcome from what changes it.
• Communicate with clarity. Prepare and deliver insight presentations and recommendations; translate complex findings and their implications for business partners and leadership.
• Build with AI as a force multiplier. Use modern AI tooling (coding assistants, LLM-based workflows) to accelerate your own development, prototyping, and analysis with sound judgment about where these tools help and where they don't.
• Help grow the practice. Serve as an analytics expert on cross-functional strategic initiatives, contribute to research and reusable methods, and help raise the bar for the broader Vanguard analytics community.

Core Qualifications

• 5+ years of applied data science / ML experience, including work that reached production or directly drove business decisions.
• Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Economics, Analytics, or a related quantitative field; graduate degree preferred, or an equivalent combination of training and demonstrated experience.
• Strong programming and data-wrangling skills in Python and SQL; comfort accessing, transforming, and preparing large-scale data for modeling.
• Solid grounding in statistical and machine learning methods, including model validation, and the judgment to choose the right method for the problem.
• Experience working in cloud environments (AWS, Azure, or GCP) and with modern collaboration/version-control tooling (e.g., Git, Jira, Confluence).
• Ability to communicate technical findings to non-technical partners and to work cross-functionally across business, engineering, and leadership.

Building for the Age of AI

• GenAI / LLM application, including retrieval-augmented generation (RAG), embeddings and semantic search, and prompt design.
• Agentic systems: designing, orchestrating, and debugging multi-step LLM/agent workflows that use tools and take actions, using frameworks such as LangChain / LangGraph or equivalents.
• LLM evaluation and reliability: building eval harnesses, defining quality and guardrail metrics, and knowing how to make non-deterministic systems trustworthy.
• Causal inference and uplift modeling: treatment-effect estimation, experimentation, and designing for "what changes the outcome," not just "what predicts it."
• MLOps mindset: model deployment, monitoring, drift detection, and the discipline of keeping a live model honest.
• Responsible AI in a regulated context: explainability, fairness, and governance awareness appropriate to financial services and to models that drive real-world actions.
• AI-augmented working style: using AI coding and analysis assistants to move faster, while critically evaluating their output rather than trusting it by default.

Preferred / Nice to Have

• Experience with recommendation, ranking, next-best-action, or other decision-support systems.
• Familiarity with feature stores, real-time or near-real-time inference, and vector databases.
• Exposure to big-data frameworks (Spark, etc.).
• Experience applying analytics across a range of business domains.

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.