Applied Data Scientist

AgenticBricks.com; Seattle (WA); On-site; USD 140,000 - 210,000; Full time

Job summary

AgenticBricks is an AI consulting firm that embeds expert engineers directly with enterprise clients to tackle complex, high-stakes challenges. We are seeking an Applied Scientist to own the ML lifecycle end to end—from feature engineering to production models and scalable inference for a large ecommerce retailer.

You will design, train, and deploy production‑grade models, manage training pipelines, and optimize serving under real‑world traffic.

Qualifications

• Graduate degree in a quantitative field or equivalent experience.
• Strong ML fundamentals and statistical literacy.
• Strong Python programming skills and ability to write production‑quality code.
• Demonstrated experience bringing models to production — feature pipelines, training, serving.
• Practical command of the full pipeline from feature engineering to inference, with failure mode awareness.
• Clear communication to non‑technical stakeholders.

Responsibilities

• Design, build, and maintain features from large‑scale retail data.
• Build reliable feature pipelines (batch and streaming) with accuracy and reuse in mind.
• Collaborate with feature stores and data infra to align training and serving.
• Train, validate, and productionize models on real production data and infra.
• Create reproducible training pipelines with versioned data and features, plus automated retraining.
• Design offline and online experiments (including A/B tests) to prove model value.
• Build and optimize model serving for production (batch and real‑time, low latency).
• Own latency, throughput, autoscaling, monitoring, and drift detection in inference.
• Diagnose production model issues and close loop to features/training.

Skills

Strong ML fundamentals

Python programming

Production‑quality code

Model deployment experience

Clear communication

Non‑technical stakeholder briefing

Education

Graduate degree in ML/CS/statistics/applied math

Tools

Feature stores

Data infrastructure

Streaming pipelines

Distributed training

Model serving infrastructure

Job description

AgenticBricks is an AI consulting firm that embeds expert engineers directly with enterprise clients to tackle complex, high-stakes challenges. We specialize in agentic systems, intelligent automation, and LLM-powered solutions that create measurable outcomes.

Applied Scientist

Type: Full‑time

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.