Data Scientist 1

We aren't looking for notebook scientists; we are looking for owners of a resilient ecosystem. At Purplle, your success is defined by your mindset and the tangible impact you have on our platform, regardless of where you are in your career.

• The Systems Thinker (Quality and Resilience): You architect self-healing ecosystems rather than building isolated features. You focus on how your models integrate into a larger, unbreakable pipeline that powers multiple products with systematic resilience.
• The AI-Fluent Architect (High Velocity): You leverage AI-augmented development to 10x your productivity. You utilise copilots and automated pipelines as force multipliers, driving a cycle time that supports high-frequency daily deployments with total confidence.
• The Data-Informed Engineer (Economic Discipline): You care about the why behind the model, connecting your work to business unit economics. You optimise ranking and recommendation logic to drive measurable outcomes in revenue and retention while maintaining economic discipline.
• The Proactive Collaborator (Security and Governance): You thrive in cross-functional environments, taking full ownership of the Golden Path. You manage your work from exploration to measurement, ensuring gated repositories and zero critical vulnerabilities reach our production environment.

Responsibilities

• Personalisation and Recommendations: Build multi-stage recommendation pipelines for candidate retrieval, scoring, and re-ranking across the homepage, PDP, cart, and cross-sell. Train user and item embeddings that capture behavioural signals and evolving preferences. Measure every change through rigorous online A/B experiments.
• Search and Ranking: Build and improve ranking models that make search results more relevant and drive more conversions. Own query understanding, intent detection, and semantic search. (Senior+) Design neural re-ranking using dense retrieval and cross-encoders.
• Demand Forecasting and Assortment Planning: Build models that predict demand at the SKU, category, and regional levels across short and long horizons. Help the business decide which products to stock, in what quantities, and for which segments. (Senior+) Build assortment intelligence systems that recommend new products to onboard based on demand gaps and market signals.
• Inventory Planning and Optimisation: Build models to predict stockouts, overstock risk, and optimal reorder points. Work with operations and category teams to reduce holding costs while maintaining availability. (Senior+) Design end-to-end inventory optimisation systems factoring in lead times, demand variability, and supplier constraints.
• GenAI, Long-Term Memory and Next Best Action: Build LLM-powered features: beauty advisors, product Q& A, and personalised content using RAG pipelines grounded in Purplle's catalogue and user data. Design long-term memory systems that remember skin type, preferences, and past interactions to power the next-best action. (Senior+) Build reflection layers so the AI learns from past user journeys and continuously improves its recommendations.
• MLOps and Model Governance: Build and maintain feature stores, model registries, and drift detection. pipelines. Implement automated retraining and shadow-mode evaluation. Every model must be observable, versioned, and rollback-capable in under 5 minutes.
• Experimentation and Causal Measurement: Run A/B tests and online experiments to measure the real business impact of model changes. (Mid+) Design experiment frameworks with power analysis and metric guardrails. (Senior+) Apply causal methods, uplift modelling, and synthetic controls to go beyond naive A/B lift.

Requirements

• DS1 (1-3 years): strong ML fundamentals and Python proficiency; exposure to GCP or AWS.

Tech stack:

• Languages: Python (primary), SQL.
• ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM.
• MLOps: Vertex AI (GCP), MLflow, Kubeflow Pipelines, Databricks.
• Feature Engineering: Feast, BigQuery ML, and dbt.
• Experimentation: Internal A/B platform, statsmodels.
• Data Processing: Apache Spark, BigQuery, Airflow.
• Infrastructure: Kubernetes, Docker, Terraform.
• AI Productivity: Cursor, Claude, and GitHub Copilot.