Data Scientist, Specialist

Vanguard•Malvern, PA

2d•Hybrid

About The Position

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.

Requirements

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.
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.
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.

Nice To Haves

Grad degree preferred.
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.).

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.