1199 | Staff Data Scientist / Machine Learning Engineer

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Machine Learning EngineerData

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Quick Summary

Key Responsibilities

- Design, build, and maintain predictive Lifetime Value (pLTV) models using machine learning techniques such as XGBoost, CatBoost, and probabilistic forecasting methods.

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

→5+ years of hands-on ML engineering in Python with production-deployed models (not just Jupyter notebooks). You should be comfortable writing production-grade code, not just prototypes.
→Experience with gradient boosted trees (XGBoost, CatBoost, or LightGBM) in a production setting, including hyperparameter tuning, feature importance analysis, and model evaluation beyond default metrics.
→Strong SQL and BigQuery fluency. You will write complex feature engineering queries, scheduled transformations, and data validation logic directly in BigQuery.
→GCP production experience. Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub. You have deployed and maintained ML workloads on GCP, not just completed certification exercises.
→Understanding of model calibration. You know the difference between a model that minimizes RMSE and a model that maximizes business value. You have experience with or strong intuition about how prediction errors compound when consumed by a downstream optimization system.
→Outbound API integration experience. You have pushed model outputs into external systems that act on them (ad platforms, recommendation engines, pricing systems, bidding systems).

Nice to Have

~1 min read

E-commerce or DTC domain experience. Customer lifetime value, purchase prediction, churn modeling, or customer segmentation for retail or subscription businesses.
Familiarity with probabilistic customer models (BG/NBD, Gamma-Gamma, or similar frameworks). If you haven't used these specifically, strong Bayesian statistics foundations are equally valuable.
Ad platform signal engineering. Experience with Google Ads API (ConversionUploadService, Enhanced Conversions), Meta Conversions API (CAPI), or similar server-side event dispatch systems.
Identity resolution or entity matching. Graph-based identity stitching, probabilistic matching, or CDP-style cross-device identity work.

Terraform, CI/CD pipeline design, Docker containerization for ML workloads.
Experience with causal inference methods, A/B testing in ad spend environments, or multi-armed bandit allocation.
Familiarity with agentic development workflows (Claude Code, Cursor, or similar AI-assisted coding tools).