at Vanguard
Asset ManagementPosted 4 days ago
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**Data Scientist, Specialist (AI-driven Analytics)** - **Key Responsibilities:** Transforms raw data into actionable insights, collaborating cross-functionally to address real business problems. - **Required Skills:** Proficient in Python and SQL, strong foundation in ML/statistics, experience with cloud environments (AWS/Azure/GCP), and comfortable using Git and Jupyter. - **Required Experience:** 3+ years, B.S. in quantitative field, and a desire for continuous learning. - **Role Summary:** In this hands-on, growth-oriented role, you'll own components of models, analyses, and features, connecting technical outputs to business value. You'll work alongside senior data scientists and stakeholders, learning to communicate complex findings clearly and effectively. - ** plusieurs Keywords:** Data-driven decisions, predictive analytics, prescriptive analytics, stakeholder communication, model deployment, AI integration
- Compensation
- Not specified USD
- City
- Malvern
- Country
- United States
Currency: $ (USD)
Full Job Description
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.
Youll 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 youll build both technical depth and the communication skills needed to become a trusted analytics partner over time.
What Youll 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
Bachelors 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 were 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 missionwe'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.
