Applied Data Scientist

About the role

We are hiring an Applied Scientist at AgenticBricks supporting a large ecommerce retailer. You'll own the core ML lifecycle end to end — feature engineering, building and training models in production, and inference at scale — for systems that serve real customers and operations at major online retail volume. This is hands-on applied work: your output is production models and the pipelines that feed and serve them, not prototypes that stop at a notebook.

What you'll do

• Feature engineering
  • 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.
• Models built in production
  • 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.
• Inference
  • 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.

What we're looking for

• Graduate degree in a quantitative field (ML, CS, statistics, applied math) or equivalent applied experience.
• Strong ML fundamentals plus the statistical literacy to evaluate models honestly.
• Strong programming skills (Python and the standard ML/data stack) and the ability to write production-quality code other engineers build on.
• Demonstrated experience taking models all the way to production — feature pipelines, training, and serving — at meaningful scale.
• Practical command of the full pipeline: feature engineering, reproducible training, and inference/serving, including the failure modes at each stage.
• Clear communication; you can explain a method, a result, and a caveat to a non-technical stakeholder.

Nice to have

• Experience in ecommerce, retail, marketplace, or large-scale consumer products.
• Hands-on work with feature stores, streaming pipelines, distributed training, or model-serving infrastructure.
• Familiarity with recommendation, search/ranking, or forecasting systems.
• MLOps depth: CI/CD for models, monitoring, versioning, and drift management in production.