AI Recommendation Systems Analyst vs AI AI Regulation Specialist

A AI Recommendation Systems Analyst Only

• Recommendation algorithm fundamentals (collaborative filtering, content-based, hybrid, deep-learning approaches)
• A/B testing design, statistical significance evaluation, and experimentation frameworks
• SQL fluency for querying large-scale behavioral and catalog datasets
• Python for data analysis, visualization, and lightweight model inference (pandas, NumPy, scikit-learn)
• Key recommendation metrics: precision@k, recall@k, NDCG, MAP, diversity, novelty, and serendipity
• User behavior analytics: session analysis, click-through modeling, conversion funnel diagnostics
• Feature engineering awareness: how features like recency, popularity, and user embeddings drive model outputs
• Data storytelling and executive communication of technical performance to non-technical stakeholders