Common mistakes with good and bad answers
#1 Jump directly to the model
Some candidates jump straight to the machine learning algorithm they would use to solve the problem, without first articulating the business application, solution goal, and success metrics.
Bad answer: “For fraud detection, I will use a deep neural network because it is powerful.”
Good answer: “Will this solution be used for real-time fraud detection with each use of the card? This means we need a fast and efficient model. Let me identify all the data that I can use for this model. First, I have transaction metadata such as transaction amount, location, and time. I also have previous transaction data for this card – I can search up to 30 days in advance to reduce the amount of data I need to analyze in real time, or I can precompute categorical/binary features derived from transaction history, such as 'is_transaction_30_days' , 'most_frequent_transaction_location_30days', etc. “Initially, I will use logistic regression to establish a baseline before considering more complex models such as deep neural networks, if necessary.”
#2 Keep the bar too high
You not only want to offer a boilerplate strategy, but also include specific examples in each step that are relevant to the given business problem.
Bad answer: “I will do exploratory data analysis, remove outliers, and build a model to predict user engagement.”
Good answer: “I will analyze historical user data, including page views, click-through rates, and time spent on the site. I will look at categorical features, such as product category and brand, and remove them if more than 75% of the values are missing. But I would be cautious in this step as the absence of some features can also be very informative at times. A logistic regression model can serve as a starting point, followed by more complex models such as Random Forest, if necessary.”
#3 Only solving the happy case
It's not difficult to recognize a lack of industry experience if the candidate only talks about data and modeling strategy without discussing data quality issues or other nuances seen in real-world data and applications.
Bad answer: “I will train a classifier using previous clicks on user items for a given search query to predict ad clicks.”
Good answer: “The user's previous clicks on items for the query may inherently have a position bias, as items displayed in higher positions in search results are more likely to be clicked. I will correct this position bias using inverse weighted propensity by estimating the click probability at each position (the propensity) and then weighting all tags with it.”
#4 Starting with the most complex models
You want to show a bias for action by using lightweight, easy-to-develop, less expensive and more time-consuming models, and introducing complexity as necessary.
Bad answer: “I will use a state-of-the-art dual-encoder deep learning architecture for the recommendation system.”
Good answer: “I'll start with a simple collaborative filtering approach to establish a baseline. “Once we understand its performance, we can introduce complexity with matrix factorization or deep learning models, such as a dual encoder, if initial results indicate the need.”
#5 Don't pivot when throwing curveballs
The interviewer may interrupt your strategy and ask follow-up questions or propose alternative scenarios to understand the depth of your understanding of the different techniques. You should be able to change your strategy as new challenges or variations are introduced.
Bad answer: “If we don't have access to the user's personally identifiable information, we can't build a custom model.”
Good answer: “For users who opt out (or opt out) of sharing their PII or data from past interactions, we may treat them as cold initial users and show them recommendations based on popularity. We can also include an online RNN session to tailor recommendations based on your activity during the session.”
Response calibration according to level
As the level of work increases, so does the expectation of breadth and depth in the response. This is best explained by an example question. Suppose you are asked to design a fraud detection system for an online payment platform.
Entry level (0 to 2 years of relevant industry experience)
For this level, the candidate should focus on data (features, preprocessing techniques), model (simple reference model, more advanced model, loss function, optimization method) and evaluation metrics (offline metrics, design of experiments A/B). A good flow would be:
- Identify characteristics and preprocessing: for example, transaction amount, location, time of day, and other categorical characteristics that represent payment history.
- Reference model and advanced model: For example, a logistic regression model as a reference, consider gradient boosted trees for the next version.
- Evaluation metrics: for example, precision, recall, F1 score.
Mid-level experience (3 to 6 years of relevant industry experience)
For this level, the candidate should focus on the business problem and the nuances in deploying models to production. A good flow would be:
- Business requirements: for example, trade-off between recall and precision, as we want to reduce the amount of fraud while keeping the false positive rate low for a better user experience; Highlight the need for interpretable models.
- Nuances in the data: For example, the number of fraudulent transactions is much lower than the number of non-fraudulent transactions; Class imbalance can be addressed using techniques such as SMOTE.
- Model tradeoffs: For example, a heuristic-based baseline model, followed by logistic regression, followed by tree-based models, as they are easier to interpret than logistic regression that uses difficult-to-use nonlinear feature transformations. interpret.
- Talk about implementation nuances: for example, real-time transaction processing and the cadence of model updates to adapt to evolving fraud patterns.
Senior/Staff/Director level experience (6+ years)
For this level, the candidate will be expected to use their multi-year experience to think critically about the broader ecosystem, identify the core challenges in this space and highlight how different ML subsystems can come together to solve the broader problem. Address challenges such as real-time data processing and ensure model robustness against adversarial attacks. Propose a multi-layered approach: rule-based systems for immediate signaling and deep learning models for pattern recognition. Include feedback loops and monitoring schemes to ensure the model adapts to new forms of fraud. Additionally, demonstrate that you are up to date with the latest industry trends where applicable (e.g., GPU usage, representation learning, reinforcement learning, edge computing, federated machine learning, building models without PII data, fairness and bias in machine learning, etc.).
