Applied Data Scientist

AgenticBricks is an AI consulting firm that embeds expert engineers directly with enterprise clients to tackle complex, high-stakes challenges. We are hiring an Applied Scientist to own the core ML lifecycle end to end for a large ecommerce retailer, focusing on feature engineering, model training, and inference at scale.

Responsibilities:

• Design, build, and maintain features from large-scale, messy retail data — transactions, catalog, behavioral signals, supply-chain and operational data
• Build reliable feature pipelines (batch and streaming) and the transformations behind them, with an eye toward correctness, freshness, and reuse across models
• Work with feature stores and data infrastructure so the same features are consistent between training and serving, and debug train/serve skew when it shows up
• Train, validate, and productionize models against real production data and infrastructure — not sandboxed datasets
• Stand up reproducible training pipelines: versioned data and features, automated retraining, and the evaluation gates that decide what ships
• Tune for the realities of scale and cost, and design the offline and online experiments (including A/B tests) that prove a model is actually better
• Build and optimize model serving for production — batch, real-time, and low-latency online inference under retail traffic loads
• Own the operational side of inference: latency, throughput, cost, autoscaling, monitoring, and drift detection
• Diagnose and fix production model issues quickly, and close the loop between what's observed in serving and what gets fixed in features or training