To succeed in the Bigbear interview, you need to deeply understand the core competencies we evaluate. Below is a breakdown of the primary areas you will be tested on and what we consider to be a strong performance.
Data Analysis and Statistics
Strong data analysis is the bedrock of everything a Data Scientist does at Bigbear. We evaluate your ability to clean, explore, and draw initial inferences from raw data. A strong performance means you do not just apply functions blindly; you understand the underlying distribution of the data, identify anomalies, and know how to handle missing values logically.
Be ready to go over:
- Exploratory Data Analysis (EDA) – Techniques for summarizing datasets, visualizing distributions, and finding correlations.
- Statistical Significance – Understanding p-values, confidence intervals, and hypothesis testing in a business context.
- Data Wrangling – Efficiently manipulating data using pandas or SQL to prepare it for modeling.
- Advanced concepts (less common) – Time-series analysis, anomaly detection techniques, and Bayesian statistics.
Example questions or scenarios:
- "Walk me through how you would handle a dataset with 30% missing values in a critical feature."
- "How do you determine if a trend you observed in an exploratory analysis is statistically significant?"
- "Given a table of user activity logs, write a SQL query to find the rolling 7-day average of active users."
Machine Learning and Modeling
We need to know that you can select, train, and validate the right models for the right problems. Interviewers will assess your understanding of the trade-offs between different algorithms. A strong candidate will prioritize model interpretability and robustness over complexity, especially given the mission-critical nature of our clients' work.
Be ready to go over:
- Algorithm Selection – Knowing when to use a random forest versus a simple logistic regression.
- Model Evaluation – Choosing the right metrics (e.g., precision, recall, F1-score, ROC-AUC) based on the specific business problem.
- Overfitting and Regularization – Techniques to ensure your model generalizes well to unseen data.
- Advanced concepts (less common) – Deep learning frameworks, natural language processing (NLP) pipelines, and model deployment strategies.
Example questions or scenarios:
- "Explain the bias-variance tradeoff and how you manage it when building a predictive model."
- "If your model is performing well on training data but poorly in production, what steps do you take to diagnose the issue?"
- "Describe a time you had to choose between a highly accurate black-box model and a slightly less accurate but fully interpretable model."
Problem Solving and Business Acumen
At Bigbear, data science is a tool to solve business and operational problems. We evaluate your ability to translate a vague request into a structured analytical plan. Strong candidates ask clarifying questions, identify the core objective, and design a solution that actually drives decision-making.
Be ready to go over:
- Metric Design – Defining what success looks like for a given project or product feature.
- Experimental Design – Structuring A/B tests or observational studies to measure impact.
- Stakeholder Communication – Explaining complex technical results to non-technical leaders.
- Advanced concepts (less common) – Causal inference and optimization algorithms.
Example questions or scenarios:
- "A client wants to predict equipment failure but has very few historical examples of failure. How do you approach this?"
- "How would you design a metric to measure the overall health of a newly deployed data pipeline?"
- "Tell me about a time you found an insightful pattern in the data, but it contradicted the business team's assumptions. How did you handle it?"
Team Culture and Values
Because you will be working on complex, high-stakes projects, how you work is just as important as what you produce. We look for adaptability, a collaborative spirit, and a strong sense of ownership. A strong performance here involves providing concrete, STAR-format examples of how you have navigated conflict, mentored others, and adapted to shifting priorities.
Be ready to go over:
- Navigating Ambiguity – How you push projects forward when requirements are unclear.
- Cross-Functional Collaboration – Working with engineers, product managers, and external clients.
- Continuous Learning – How you stay updated with industry trends and apply new techniques to your work.
- Advanced concepts (less common) – Leading technical initiatives or driving cultural changes within a data team.
Example questions or scenarios:
- "Describe a time when you had to pivot your analytical approach halfway through a project due to changing requirements."
- "Tell me about a situation where you had to explain a complex machine learning concept to a non-technical stakeholder."
- "How do you handle situations where you disagree with an engineering counterpart on how to implement a model?"
