OpenAI's Data Scientist interview includes: 1) Phone screening with ML concepts and research discussion (60 min), 2) Technical deep-dive covering experimental design and AI safety (90 min), 3) On-site loop with coding challenges, research presentation, AI alignment discussions, and behavioral rounds. You'll solve ML evaluation problems, design safety experiments, discuss recent AI research, and demonstrate understanding of alignment challenges. Focus on rigorous experimental methodology and safety considerations.

Essential skills include: advanced statistics and experimental design, machine learning evaluation methodologies, AI safety and alignment concepts, bias detection and fairness metrics, and large-scale data analysis. Key areas: LLM evaluation techniques, human feedback incorporation, Constitutional AI principles, scaling laws, and responsible AI deployment. Strong programming skills in Python, experience with ML frameworks, and familiarity with transformer architectures are valuable.

OpenAI research problems include: model alignment evaluation ('Design metrics for AI system alignment'), bias analysis ('Detect and measure bias in LLM outputs'), safety monitoring ('Build systems to detect harmful model behavior'), capability assessment ('Measure model performance across domains'), and human preference learning ('Analyze user feedback to improve models'). Emphasize rigorous methodology, safety considerations, and practical implementation.

AI safety and alignment knowledge is crucial. Key areas include: Constitutional AI principles, RLHF (Reinforcement Learning from Human Feedback), AI alignment problem formulations, interpretability techniques, and robustness evaluation. Study OpenAI's safety research, understand alignment challenges, learn about reward modeling, and show commitment to beneficial AI development. Demonstrate ability to balance capability advancement with safety considerations.

OpenAI Data Scientist compensation (2024 data): Research Scientist: $160k-220k base, $280k-450k total; Senior Research Scientist: $200k-280k base, $400k-650k total; Principal Research Scientist: $250k-350k base, $500k-800k total. Includes base salary, equity with high growth potential, and research bonuses. Excellent benefits, conference attendance, and research publication support. Career growth through research leadership, specialization in safety/alignment, or transition to research management roles.

Multi-dimensional: helpfulness vs harmlessness trade-offs, calibration, robustness to adversarial prompts, hallucination rate, latency/cost efficiency, preference alignment scores, content safety thresholds. Expect composite dashboards rather than a single metric.

Understand stages: supervised fine-tuning, preference data collection, reward model training, reinforcement learning (PPO/DPO variants), evaluation loops. Discuss reward hacking risks, distribution shift, and how you'd design better preference data quality controls.

Expect strong emphasis on reproducibility (versioned datasets, seed control), statistical validity (power, multiple test correction), ablation studies, and reporting uncertainty. Be ready to critique an experiment's methodology and propose stronger baselines.

Dimensions: experimental design rigor, ML evaluation creativity, safety/alignment awareness, statistical inference depth, ability to translate research signals into product metrics, bias/fairness mitigation strategies, and clear communication of uncertainty + trade-offs. Coding focuses on analytical clarity over trick puzzles.