Senior/Data Scientist

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

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