CHARLES & KEITH GROUP

We are seeking a Senior/Data Scientist with hands-on experience on the AWS platform to build and operate production machine learning. The immediate priority is our real-time hyper-personalization engine - a contextual multi-armed bandit built on Amazon SageMaker AI - and the role extends beyond it to generative AI and broader data science projects across the Group.

 

Responsibilities:

 

Contextual Bandit Personalization on AWS (Flagship Project)

•Design, build, and tune a contextual multi-armed bandit personalizing homepage, listing, product, and cart pages to lift conversion rate and AOV

•Engineer behavioral features from clickstream and warehouse data, design reward functions, and tune exploration/exploitation policies per surface

•Deliver end-to-end on Amazon SageMaker AI — training jobs, Pipelines, Model Registry, Feature Store, Model Monitor, and real-time endpoints (sub-100 ms)

•Validate uplift through controlled A/B experimentation, and take the system over from the delivery vendor into production ownership after go-live

 

Generative AI and Broader Data Science Projects

•Build production generative AI applications for retail - RAG over product catalogs and enterprise data, agentic workflows, content and service copilots - with evaluation, guardrails and cost control

•Deliver wider data science: demand forecasting, customer lifetime value, pricing and markdown, search, recommendations and segmentation

 

Engineering and Operations

•Build with production discipline: versioned pipelines, infrastructure-as-code, CI/CD for ML, containerization, security and cost control

•Monitoring, drift detection, retraining, and incident response

 

Requirements:

•Bachelor’s Degree in Computer Science, Machine Learning, Data Science, or related field

•4+ years of applied ML in production for the Data Scientist level, or 7+ years for the Senior level, including personalization, recommendation or decisioning systems at consumer scale

•Hands-on experience delivering machine learning on the AWS platform - Amazon SageMaker AI end-to-end (training jobs, Pipelines, Model Registry, Feature Store, Model Monitor, real-time endpoints)

•Broader AWS stack (S3, Glue, Athena, Kinesis, Lambda, Step Functions, IAM, KMS) plus MLOps: CI/CD for ML, IaC, containers, and observability

•Contextual bandits or reinforcement learning (LinUCB, Thompson Sampling): reward design, exploration, cold start, and off-policy evaluation; strong recommender-system depth also considered

•Practical generative AI experience (prompting, RAG, fine-tuning, evaluation, guardrails); Python and SQL, PyTorch/TensorFlow, Hugging Face, LangChain, and vector databases

•Rigorous A/B testing practice and excellent communication across business and technical teams

• Fashion retail or retail/ECommerce background, fluent in retail metrics and processes (conversion funnel, AOV, merchandising, seasonality) is an advantage

•Good to have: AWS Certified Machine Learning - Specialty or ML Engineer – Associate; Amplitude and Salesforce Commerce Cloud familiarity

Amazon

Data

Data Science

Machine Learning

Continuous Integration

Security software

Amazon S3

Python

Database

Testing

TensorFlow

System security

Cloud

Forecasting

Experimentation

Containers

PyTorch

Communications

E-commerce

Salesforce

AWS SageMaker

Huggingface

Langchain

Vectorization

CI/CD

Observability

Aws athena

Lambda

Aws step functions

Aws iam

PKMS

Senior/Data Scientist