To succeed in the AURORA interview process, you must excel across several distinct technical and behavioral domains. Below is a breakdown of the core evaluation areas.
Statistical Analysis & Probabilistic Modeling
At the heart of safety data science is the ability to quantify uncertainty and model complex, often rare, events. This area evaluates your depth in statistics and your ability to apply mathematical rigor to real-world autonomous driving data. Strong performance means you do not just know the formulas; you know exactly when and why to apply specific models to support a safety case.
Be ready to go over:
- Hypothesis Testing & Experimentation – Designing robust A/B tests and observational studies to evaluate the safety impact of new software releases.
- Probabilistic Modeling – Modeling rare events, such as hardware failures or edge-case traffic scenarios, using appropriate distributions.
- Survival Analysis & Reliability Engineering – Estimating the time-to-failure for vehicle components and understanding degradation over time.
- Advanced concepts (less common) – Bayesian inference, Markov Decision Processes, and causal inference methods to isolate the impact of specific autonomous behaviors.
Example questions or scenarios:
- "How would you model the probability of a specific sensor failure occurring during a 100-mile autonomous trip?"
- "If we roll out a new perception algorithm, how would you statistically prove that it reduces the rate of near-miss incidents?"
- "Explain how you would handle extreme class imbalance when trying to predict rare, catastrophic safety events."
Safety Metrics & Risk Assessment
Because this role sits on the Safety Analysis team, your domain intuition is heavily scrutinized. Interviewers want to see how you define, measure, and forecast risk. A strong candidate can seamlessly bridge the gap between raw vehicle logs and high-level safety strategy.
Be ready to go over:
- Leading vs. Lagging Indicators – Developing predictive metrics (leading) based on historical crash or near-miss data (lagging).
- Hazard Analysis – Identifying critical risk factors in complex operational domains.
- Data Integration – Combining proprietary data (sensor logs, integration tests) with public datasets (CRSS, FARS, state-level info) to create comprehensive risk profiles.
Example questions or scenarios:
- "Design a leading indicator for autonomous vehicle safety based on historical near-miss incident data."
- "How would you integrate state-level crash data (like FARS) with our proprietary simulation data to assess the risk of a new operational design domain?"
- "Walk me through how you would conduct a quantitative hazard assessment for a new autonomous trucking route."
Data Engineering & Automation
You cannot analyze data if you cannot efficiently extract and process it. AURORA expects its Data Scientists to be self-sufficient in manipulating large-scale datasets. Strong performance involves writing clean, optimized code to automate data collection and analysis pipelines.
Be ready to go over:
- SQL & Relational Databases – Writing complex queries, utilizing window functions, and optimizing joins for massive datasets.
- Python/R Programming – Using pandas, numpy, or equivalent libraries for data manipulation and statistical analysis.
- Pipeline Automation – Designing automated workflows to track ongoing safety programs and operational vehicle metrics.
Example questions or scenarios:
- "Write a SQL query to extract and aggregate crash metrics from a highly nested operational vehicle dataset over a rolling 30-day window."
- "How would you design an automated data pipeline to continuously monitor and flag anomalies in vehicle sensor data?"
Technical Communication & Stakeholder Management
As a Senior or Staff-level contributor, your impact is measured by your ability to influence decisions. This area evaluates your communication skills, leadership, and ability to present complex findings to both technical and non-technical audiences.
Be ready to go over:
- Cross-Functional Influence – Persuading engineering, operations, and product teams to alter roadmaps based on safety data.
- External Communications – Authoring benchmark safety studies for authoritative bodies and industry forums.
- Navigating Ambiguity – Driving consensus when data is incomplete or stakeholders have conflicting priorities.
Example questions or scenarios:
- "Tell me about a time you had to convince an engineering team to change a product decision based on your safety analysis."
- "How would you explain a complex probabilistic risk model to a non-technical regulatory stakeholder?"
