To excel in the Leidos interview process, you must understand the specific areas where you will be evaluated. The hiring team wants to see that you are not just a user of machine learning tools, but a practitioner who understands the "why" behind every technical decision.
Machine Learning & Deep Learning Foundations
This area evaluates your theoretical understanding of algorithms, model architectures, and statistical concepts. You need to demonstrate that you can select, build, and tune models based on solid scientific principles rather than trial and error.
Be ready to go over:
- Model selection criteria – Explaining why a specific algorithm (e.g., Random Forest vs. XGBoost) is appropriate for a given dataset and business constraint.
- Regularization techniques – How L1 and L2 regularization work, and how they prevent overfitting in linear and deep models.
- Evaluation metrics – Choosing the right metrics (e.g., F1-score, ROC-AUC, Precision-Recall) for imbalanced datasets, which are highly common in defense and intelligence applications.
- Advanced concepts (less common) – Deep learning architectures like transformers, autoencoders for anomaly detection, and reinforcement learning principles.
Example questions or scenarios:
- "If you are training a deep neural network and notice that your training loss is decreasing but your validation loss is increasing, what steps would you take to diagnose and fix this?"
- "How would you design a validation strategy for a time-series forecasting model to ensure there is no data leakage?"
Data Manipulation & Transformations
Before any modeling can occur, data must be ingested, cleaned, and structured. This evaluation area focuses on your ability to work with messy, real-world data, including unique file formats and incomplete records.
Be ready to go over:
- Feature engineering – Transforming raw data into meaningful features that boost model performance.
- Data cleaning pipelines – Handling missing values, outliers, and mismatched data types efficiently.
- File parsing – Writing clean code to read and manipulate non-standard file formats or unstructured text.
Example questions or scenarios:
- "Walk me through how you would parse and clean a large dataset containing nested JSON structures and inconsistent timestamps."
- "What are the pros and cons of different imputation strategies for missing numerical data in a sensor dataset?"
System Walkthroughs & Resume Defense
Because the interviewers will heavily scrutinize your past work, you must be prepared to defend every technical detail of the projects listed on your resume. They want to see that you took ownership of your past projects and understand their full lifecycle.
Be ready to go over:
- Architectural choices – Why you built a pipeline or model in a specific way.
- Business or mission impact – How your data science solution solved the original problem or improved operational efficiency.
- Collaboration – How you worked with other engineers and stakeholders to deploy your models.
Example questions or scenarios:
- "On your resume, you mentioned deploying a deep learning model for image classification. Can you explain the exact architecture you used and how you handled model deployment?"
- "Describe a time when your model did not perform as expected in production. How did you identify the issue, and what did you do to resolve it?"
