FiscalNote Machine Learning Engineer Interview Questions + Guide in 2025

FiscalNote is a technology company that provides data-driven insights to help organizations navigate the complex landscape of government and public policy.

As a Machine Learning Engineer at FiscalNote, you will be responsible for designing, developing, and deploying machine learning models that enhance the company’s ability to analyze legislative data and deliver actionable insights to clients. Key responsibilities include implementing algorithms, optimizing existing models, and collaborating with cross-functional teams to integrate machine learning solutions into products. Strong skills in algorithms, Python, and machine learning are essential, along with a solid understanding of statistics and data analysis. A great fit for this role embodies a passion for technology, excellent problem-solving skills, and the ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

This guide aims to provide you with a tailored framework to excel in your interview by focusing on the nuances of the role and the company's mission.

The interview process for a Machine Learning Engineer at FiscalNote is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several stages:

1. Initial Phone Screening

The first step is a brief phone screening with a recruiter, lasting around 15-30 minutes. During this call, the recruiter will discuss your background, experience, and motivations for applying to FiscalNote. This is also an opportunity for you to ask questions about the role and the company culture.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a coding challenge or a project that tests your machine learning skills, algorithms, and problem-solving abilities. The assessment is designed to evaluate your proficiency in Python and your understanding of machine learning concepts. Candidates should be prepared to demonstrate their ability to design and implement algorithms effectively.

3. Panel Interview

The next stage typically consists of a panel interview, which may include multiple team members from the engineering department. This interview focuses on both technical and behavioral questions. You can expect to discuss your previous projects, your approach to troubleshooting, and how you handle client-facing situations. This is also a chance to showcase your communication skills and your ability to work collaboratively within a team.

4. Final Interview

The final stage often involves a more informal meeting with senior team members or executives. This is an opportunity for you to ask deeper questions about the company’s vision, culture, and future projects. It may also include discussions about your long-term career goals and how they align with the company’s objectives.

Throughout the process, candidates should be prepared for a variety of questions that assess both technical expertise and interpersonal skills, as well as their passion for technology and customer service.

Now, let's delve into the specific interview questions that candidates have encountered during their interviews at FiscalNote.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at FiscalNote typically involves multiple stages, starting with an initial phone screening followed by a series of interviews that may include technical assessments and project discussions. Familiarize yourself with this structure so you can prepare accordingly. Be ready to showcase your technical skills, particularly in algorithms and Python, as these are crucial for the Machine Learning Engineer role.

Showcase Your Technical Proficiency

Given the emphasis on algorithms and Python, ensure you are well-versed in these areas. Prepare to discuss your experience with machine learning frameworks and libraries, and be ready to solve coding challenges on the spot. Practice coding problems that require you to implement algorithms efficiently, as this will likely be a focus during technical interviews.

Prepare for Behavioral Questions

Expect a mix of technical and behavioral questions. Be prepared to discuss your past experiences, particularly how you’ve handled challenges or resolved conflicts. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your problem-solving skills and ability to work collaboratively.

Communicate Your Passion

FiscalNote values candidates who are passionate about technology and its impact on customer service. Be sure to convey your enthusiasm for machine learning and how it can drive innovation within the company. Share specific examples of projects or experiences that demonstrate your commitment to the field.

Be Ready for Project Discussions

You may be asked to present a project you’ve worked on, so choose one that showcases your skills in machine learning and algorithms. Be prepared to discuss your methodology, the challenges you faced, and the outcomes. This will not only demonstrate your technical abilities but also your capacity to communicate complex ideas effectively.

Engage with the Interviewers

During your interviews, take the opportunity to ask insightful questions about the team, company culture, and future projects. This shows your interest in the role and helps you assess if FiscalNote is the right fit for you. Questions about how the team collaborates on machine learning projects or how they measure success can provide valuable insights.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the position and briefly mention any key points you may want to emphasize again. A thoughtful follow-up can leave a positive impression and keep you top of mind for the hiring team.

By preparing thoroughly and approaching the interview with confidence and enthusiasm, you can position yourself as a strong candidate for the Machine Learning Engineer role at FiscalNote. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at FiscalNote. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of machine learning concepts. Be prepared to discuss your experience with algorithms, Python, and your approach to real-world data challenges.

Algorithms

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, including their applications and the types of problems they solve.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in real-world scenarios.

How to Answer

Highlight a specific project, the challenges encountered, and how you overcame them, focusing on your role and contributions.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to generate synthetic samples and improved the model's performance significantly.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics and their importance.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using multiple metrics. For classification tasks, I focus on precision and recall to understand the trade-offs between false positives and false negatives. For regression tasks, I often use RMSE to assess prediction accuracy.”

4. What is overfitting, and how can it be prevented?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization. To prevent it, I use techniques like cross-validation to ensure the model performs well on unseen data and apply regularization methods to penalize overly complex models.”

5. Can you explain the concept of feature engineering?

Feature engineering is a critical skill for machine learning engineers.

How to Answer

Discuss the importance of selecting and transforming features to improve model performance.

Example

“Feature engineering involves creating new input features from existing data to enhance model performance. For instance, in a time series analysis, I might extract features like the day of the week or month to capture seasonal trends.”

Python

1. What libraries do you commonly use for machine learning in Python?

This question assesses your familiarity with Python libraries relevant to machine learning.

How to Answer

Mention popular libraries and their specific use cases in your projects.

Example

“I frequently use libraries like Scikit-learn for building models, Pandas for data manipulation, and NumPy for numerical operations. For deep learning, I rely on TensorFlow and Keras.”

2. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data preprocessing.

How to Answer

Discuss various strategies for dealing with missing data, including imputation and removal.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or if the missing data is substantial, I may choose to remove those records entirely.”

3. Can you write a function to implement a basic linear regression model?

This question tests your coding skills and understanding of linear regression.

How to Answer

Explain the steps involved in implementing linear regression, focusing on the logic rather than the syntax.

Example

“I would start by importing necessary libraries, then define a function that takes in training data and labels, calculates the coefficients using the least squares method, and finally returns the predictions based on the input features.”

4. Describe how you would optimize a machine learning model in Python.

This question evaluates your approach to model optimization.

How to Answer

Discuss techniques such as hyperparameter tuning, feature selection, and using cross-validation.

Example

“To optimize a model, I would first perform hyperparameter tuning using GridSearchCV to find the best parameters. Additionally, I would analyze feature importance and remove irrelevant features to improve model efficiency and accuracy.”

5. How do you ensure your code is maintainable and scalable?

This question assesses your coding practices and software engineering skills.

How to Answer

Discuss best practices such as code modularity, documentation, and version control.

Example

“I ensure my code is maintainable by following best practices like writing modular functions, using clear naming conventions, and documenting my code thoroughly. I also use version control systems like Git to track changes and collaborate effectively.”

Statistics & Probability

1. What is the Central Limit Theorem, and why is it important?

Understanding statistical concepts is crucial for data analysis.

How to Answer

Define the Central Limit Theorem and explain its significance in statistics.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is important because it allows us to make inferences about population parameters using sample statistics.”

2. Explain the difference between Type I and Type II errors.

This question tests your understanding of hypothesis testing.

How to Answer

Define both types of errors and their implications in statistical testing.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Understanding these errors is crucial for interpreting the results of hypothesis tests accurately.”

3. How do you interpret a p-value?

This question assesses your knowledge of statistical significance.

How to Answer

Explain what a p-value represents in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating statistical significance.”

4. What is the purpose of A/B testing?

A/B testing is a common method for evaluating changes in a system.

How to Answer

Discuss the concept of A/B testing and its applications.

Example

“A/B testing is used to compare two versions of a webpage or product to determine which one performs better. By randomly assigning users to different groups and measuring their responses, we can make data-driven decisions to optimize user experience.”

5. Can you explain the concept of Bayesian statistics?

This question tests your understanding of different statistical paradigms.

How to Answer

Define Bayesian statistics and its advantages over traditional methods.

Example

“Bayesian statistics incorporates prior knowledge or beliefs into the analysis, allowing for more flexible modeling. It updates the probability of a hypothesis as more evidence becomes available, which is particularly useful in dynamic environments.”