AI Engineer
Marketing
Python
Algorithms
Analytics
Artificial Intelligence
Bandit Algorithms
Business Intelligence
Causal Inference
Data Analysis
Data Analytics
Data Platform
Data Processing
Data Science
Data Visualization
Database
Databases
Difference In Differences
Digital Marketing
Feature Engineering
Information Technology (IT)
Machine Learning Engineer
Marketing Analytics
Model Experimentation
Multivariate Testing
Probabilistic Programming
Production Analytics
Programming Language
Programming Languages
Real Time Analytics
Reporting and Analytics
Shapley Value
SQL
Statistical Analysis Software
Statistical Modeling
Visual Design
Job Description
Develop and own algorithms for budget allocation, attribution, and rigorous experimental measurement in a remote AI engineering role.
Responsibilities
- Build and maintain a multi-armed bandit engine using Thompson Sampling on Beta-Bernoulli reward models, with contextual extensions as needed; tune exploration versus exploitation, handle cold-start scenarios, and monitor non-stationary reward drift.
- Implement attribution with Shapley value for multi-touch credit assignment and compare against first-touch, last-touch, and position-based baselines.
- Design and operate experiment infrastructure for A/B and multivariate tests, including power analysis, sequential testing with proper alpha spending, and significance testing that remains valid under peeking.
- Conduct cross-channel causal measurement using geo holdouts, incrementality tests, and difference-in-differences designs to isolate true interaction effects from correlation.
Requirements
- Strong Python skills with NumPy, SciPy, pandas, and scikit-learn.
- Hands-on experience with multi-armed bandits including Thompson Sampling, UCB, and epsilon-greedy; understands regret bounds beyond API usage.
- Expertise in hypothesis testing and experimental design: power analysis, multiple comparisons correction, sequential testing, and the peeking problem.
- Production-scale A/B testing experience; you have shipped tests, not only analyzed them.
- Proficiency in causal inference methods such as DiD, synthetic control, instrumental variables, or incrementality testing.
- Advanced SQL skills with PostgreSQL.
Technologies
- Python
- NumPy
- SciPy
- pandas
- scikit-learn
- PostgreSQL
- PyMC
- Stan
- NumPyro
Benefits
- Pay: $60.00 - $70.00 per hour
- Remote
You’ll fit if
- You expect a control group before reporting lift numbers and can explain the difference between correlation and causation to a CMO without condescension.
Strongly preferred
- Marketing mix modeling or media measurement background
- Bayesian methods with PyMC, Stan, or NumPyro
- Shapley values or cooperative game theory applied to attribution
- Production ML deployment experience, not just notebooks
Similar Jobs
