Premier Data Scientist

Entry Level

In Office / Hybrid

About the role:

As part of Team Amex’s Global Data / Decision-Science organization, you’ll work on large-scale data and machine-learning solutions that power high-impact decisions — from credit & fraud risk, customer behaviour analysis, to marketing, personalization and product-experience optimization. You’ll be part of an advanced analytics / AI/ML-driven team tasked with building, deploying and maintaining predictive models and delivering actionable insights that influence global business and risk decisions.

Responsibilities & Impact

Collect, clean, preprocess and transform large and complex datasets (structured and unstructured — web/app/API/transaction data, etc.) to prepare for modeling.
Design, build, validate and deploy predictive models and ML/AI algorithms (for risk-scoring, fraud detection, customer segmentation, recommendation systems, marketing analytics, behavioural predictions, etc.) to drive profitable decisions, reduce risk, and improve user experience.
Collaborate with business stakeholders, product owners, engineering/tech teams to translate business problems into data science solutions — and deliver insights to inform strategy and execution.
Monitor and maintain models in production: track performance, retrain/validate as required, ensure robustness and compliance (especially critical for finance/risk-oriented models).
Engage with latest research and industry advancements (ML/AI/GenAI/NLP) — test and experiment with new algorithms and frameworks; push innovation internally to improve existing solutions or create new data products.
Present results, model outcomes, insights, and recommendations clearly to both technical peers and non-technical leadership/business teams; communicate implications and guide decisions.

What Amex Looks for — Skills & Qualifications

Strong programming and data-handling skills: Python (or R/Java), SQL/Hive/Spark, experience with big-data tools or distributed computation environments.
Solid grounding in statistics, mathematics, and machine-learning — including supervised/unsupervised learning, predictive modelling, possibly deep learning or NLP/ML for advanced roles.Capacity to wrangle large datasets, perform exploratory data analysis (EDA), feature engineering, data visualization, and derive business-relevant insights from data.
Ability to think like a researcher and problem-solver: experimentation mindset, willingness to explore new ML/AI techniques or algorithms, academic curiosity — especially valued in global decision-science or AI labs teams.
Strong communication skills — to articulate complex data-science/machine-learning results in a clear, business-oriented way; collaborate with cross-functional teams.
For senior or leadership-level: experience in delivering end-to-end solutions (from prototype to production), working in agile / fast-paced environments, possibly cloud infrastructure exposure (AWS/GCP), ability to handle ambiguity and deliver under dynamic business conditions.