Senior/Data Scientist

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

*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