Learning Roadmap
How to Become a AI A/B Testing Analyst
A step-by-step, phase-based learning path from beginner to job-ready AI A/B Testing Analyst. Estimated completion: 6 months across 5 phases.
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Foundations of Experimentation & Statistics
4 weeksGoals
- Understand hypothesis testing, p-values, confidence intervals, and effect sizes
- Learn basic SQL for data extraction and Python for statistical analysis
- Grasp the end-to-end A/B testing lifecycle from design to decision
Resources
- Udacity: A/B Testing by Google (free course)
- Book: 'Trustworthy Online Controlled Experiments' by Kohavi, Tang, and Xu
- Khan Academy: Statistics and Probability modules
- Mode Analytics SQL Tutorial
MilestoneYou can design a simple A/B test, write a power analysis, and analyze results with Python using scipy and statsmodels.
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AI Product Evaluation & LLM-Specific Testing
6 weeksGoals
- Learn how LLM non-determinism complicates traditional experimentation
- Master prompt engineering to create structured test variants
- Build evaluation harnesses using OpenAI Evals and HuggingFace Evaluate
Resources
- OpenAI Cookbook: Evals and grading
- LangChain documentation on evaluation and tracing (LangSmith)
- HuggingFace Evaluate library documentation
- Anthropic research papers on constitutional AI evaluation
MilestoneYou can design and run an LLM evaluation experiment comparing prompt variants or model versions with statistically sound methodology.
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Advanced Experimentation & Multi-Armed Bandits
4 weeksGoals
- Learn Bayesian A/B testing and sequential analysis for faster decisions
- Understand multi-armed bandit algorithms (Thompson Sampling, UCB)
- Study causal inference methods for observational AI feature studies
Resources
- Book: 'Bayesian Methods for Hackers' (free online)
- Evan Miller's blog on sequential testing and always-valid p-values
- Google's 'Causal Inference' course on Coursera
- Statsig documentation on dynamic holdouts and layers
MilestoneYou can implement Bayesian experiment analysis and recommend bandit strategies for dynamic AI feature optimization.
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Production Systems & Cross-Functional Impact
4 weeksGoals
- Build end-to-end experiment dashboards with Looker, Tableau, or Hex
- Learn experiment platform architecture (feature flags, segmentation, guardrails)
- Develop communication skills for presenting experiment findings to stakeholders
Resources
- LaunchDarkly documentation on feature flag experiments
- Amplitude Experiment and Mixpanel Experiments guides
- Hex or Observable for collaborative data notebooks
- Book: 'Storytelling with Data' by Knaflic
MilestoneYou can build a production-grade experiment reporting pipeline and present actionable insights to product and engineering leadership.
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Specialization & Portfolio Building
4 weeksGoals
- Complete 3-5 portfolio projects showcasing AI experimentation expertise
- Contribute to open-source AI evaluation tooling
- Prepare for interviews with scenario-based practice
Resources
- GitHub: open-source experiment analysis libraries (e.g., Spotify's PlanOut, Microsoft's ExP)
- Kaggle: datasets for experimentation practice
- Personal blog or portfolio site documenting experiment case studies
- Mock interview platforms (Interviewing.io, Pramp)
MilestoneYou have a polished portfolio demonstrating end-to-end AI experimentation projects and are interview-ready for mid-level roles.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
LLM Prompt Variant A/B Test Dashboard
BeginnerBuild a Python-based analysis pipeline that compares two prompt variants for a text generation task. Generate synthetic data, compute quality metrics (using automated scoring), run statistical tests, and visualize results in a Jupyter notebook dashboard.
Automated LLM-as-Judge Evaluation Framework
IntermediateCreate a reusable evaluation framework using OpenAI's API to score LLM outputs against a structured rubric. Include calibration against human ratings, bias detection, and batch processing capabilities. Test it by comparing two models on a customer support dataset.
Multi-Armed Bandit for Prompt Optimization
IntermediateImplement a Thompson Sampling bandit algorithm in Python that dynamically allocates traffic across 10 prompt templates for a content generation task, converging on the best-performing variant while minimizing opportunity cost.
End-to-End Experiment Platform Prototype
AdvancedBuild a mini experiment platform using Python/Flask that handles user assignment via hashing, exposure logging, feature flag delivery, and automated analysis. Integrate with a SQL database and include a dashboard for monitoring experiment health.
Causal Impact Analysis of an AI Feature Launch
AdvancedUsing a publicly available dataset (e.g., an e-commerce dataset), simulate a non-randomized AI feature rollout and apply causal inference methods (difference-in-differences, synthetic control) to estimate the feature's true impact, comparing results to a naive before-after analysis.
AI Model Migration Shadow Test
IntermediateDesign and implement a shadow testing pipeline where a new model processes the same inputs as the production model without serving results to users. Build automated quality comparison reports and latency benchmarking. Use LangSmith or W&B for tracking.
Ready to Start Your Journey?
Prep for interviews alongside your learning — it reinforces every concept.