New American Funding Data Scientist Interview Guide

Getting ready for a Data Scientist interview at New American Funding? The New American Funding Data Scientist interview process typically spans a wide range of question topics and evaluates skills in areas like data analysis, machine learning, programming, and business problem-solving. Interview prep is especially important for this role at New American Funding, as candidates are expected to demonstrate not only technical proficiency but also the ability to translate complex data into actionable insights for business stakeholders in a fast-paced, highly regulated financial environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Scientist positions at New American Funding.
• Gain insights into New American Funding’s Data Scientist interview structure and process.
• Practice real New American Funding Data Scientist interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the New American Funding Data Scientist interview process, along with sample questions and preparation tips tailored to help you succeed.

New American Funding is a leading mortgage lender specializing in home loans, refinancing, and mortgage solutions across the United States. Founded by Rick and Patty Arvielo, the company leverages advanced technology and streamlined operations to maximize lending efficiency and deliver exceptional customer service. Recognized among the Top 100 Mortgage Companies in America and featured on the Inc. 5000 list of fastest-growing companies, New American Funding is committed to innovation and in-house loan processing. As a Data Scientist, you will contribute to the company’s mission by using data-driven insights to optimize lending processes and enhance customer experiences.

As a Data Scientist at New American Funding, you will leverage advanced analytics, machine learning, and statistical modeling to extract insights from large datasets related to the mortgage and financial services industry. You will work closely with business, operations, and technology teams to develop predictive models that inform lending strategies, risk assessment, and customer engagement initiatives. Key responsibilities include cleaning and analyzing data, building and validating models, and communicating findings to stakeholders to drive data-driven decision-making. This role is essential in supporting New American Funding’s mission to deliver efficient, customer-focused mortgage solutions through innovative use of data and analytics.

2.1 Stage 1: Application & Resume Review

The process begins with an in-depth review of your application materials by the recruiting team, focusing on your experience in data science, statistical modeling, machine learning, and your ability to communicate technical concepts to non-technical stakeholders. Special attention is given to demonstrated skills in Python, SQL, data cleaning, and experience with large, complex datasets relevant to financial services or consumer lending. To prepare, tailor your resume to showcase quantifiable impact, highlight end-to-end data project ownership, and emphasize collaborations with cross-functional teams.

2.2 Stage 2: Recruiter Screen

A recruiter will conduct an initial phone or video call to discuss your background, motivation for joining New American Funding, and alignment with the company’s mission. Expect questions about your interest in financial technology, your understanding of the company’s products, and your overall fit. Preparation should include researching the company’s business model, recent initiatives, and reflecting on how your data science background can add value to their goals.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or two rounds with data scientists or analytics managers. You may encounter live technical assessments, case studies, or take-home assignments. Expect to be evaluated on your ability to write efficient SQL queries, perform data cleaning and transformation, design and implement machine learning models (such as logistic regression or classification), and solve business problems using data-driven approaches. You may also be asked to discuss system design for data pipelines, analyze experimental results (like A/B testing), and handle large-scale data integration. Preparation should focus on practicing coding, reviewing end-to-end project workflows, and developing frameworks for tackling open-ended business cases.

2.4 Stage 4: Behavioral Interview

A hiring manager or senior team member will assess your interpersonal skills, adaptability, and communication style. You’ll be expected to share examples of how you’ve navigated project hurdles, resolved stakeholder conflicts, and made complex data insights accessible to non-technical audiences. The best preparation is to develop concise STAR-format stories highlighting your impact, leadership, and ability to demystify data for business partners.

2.5 Stage 5: Final/Onsite Round

The final stage may consist of a series of interviews—either virtual or onsite—typically involving multiple team members from data science, engineering, and product. Sessions may include technical deep-dives, whiteboarding exercises, presentations of prior work, and scenario-based questions on data-driven decision-making. You’ll be evaluated on collaboration, technical depth, and your ability to translate data analysis into actionable recommendations for business growth and risk reduction. Prepare by reviewing your portfolio, practicing technical presentations, and anticipating cross-functional questions.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete the interviews, the recruiter will reach out with an offer. This stage covers compensation, benefits, and start date negotiation. Preparation should include market research on salary benchmarks for data scientists in the financial sector and a clear understanding of your priorities and value proposition.

2.7 Average Timeline

The typical New American Funding Data Scientist interview process spans 3 to 5 weeks from initial application to offer, with most candidates moving through one stage per week. Fast-track candidates with highly relevant experience may complete the process in as little as 2-3 weeks, while the standard pace allows for more scheduling flexibility and additional rounds as needed. Take-home assessments and onsite scheduling can add a few days depending on candidate and team availability.

Next, let’s dive into the types of interview questions you can expect throughout the process.

3.1 Machine Learning & Modeling

Expect scenario-based questions that assess your ability to design, evaluate, and communicate predictive models. Focus on clearly articulating your approach to problem definition, feature selection, validation, and business impact.

3.1.1 Identify requirements for a machine learning model that predicts subway transit
Outline how you would gather and preprocess the necessary data, select relevant features, and choose an appropriate modeling technique. Discuss how you would evaluate model performance and address real-world constraints.

Example answer: "I'd start by collecting historical transit data, weather, and event schedules, then engineer features like time of day and station location. I’d use a tree-based model for interpretability, validate with cross-validation, and monitor accuracy against actual ridership trends."

3.1.2 Building a model to predict if a driver on Uber will accept a ride request or not
Describe your approach to data preparation, feature engineering, and model selection. Emphasize how you would handle class imbalance and evaluate performance.

Example answer: "I’d compile data on driver history, location, and request timing, then balance the dataset using techniques like SMOTE. I’d train a logistic regression or random forest, optimizing for recall to minimize missed matches."

3.1.3 Designing an ML system to extract financial insights from market data for improved bank decision-making
Explain how you would architect a system that ingests external APIs, processes raw data, and delivers actionable insights. Discuss considerations for reliability, scalability, and regulatory compliance.

Example answer: "I’d integrate financial APIs into a secure ETL pipeline, apply feature extraction, and deploy models for risk prediction. I’d ensure the system logs all transformations and supports real-time dashboards for decision-makers."

3.1.4 Implement logistic regression from scratch in code
Summarize the key steps in building a logistic regression model, including initialization, gradient descent, and evaluation metrics. Stress the importance of interpretability and reproducibility.

Example answer: "I’d initialize weights, iterate using gradient descent to minimize log-loss, and evaluate with ROC-AUC and confusion matrices. Clean code and modular design would allow easy extension to new datasets."

3.2 Data Analysis & Experimentation

These questions gauge your skills in drawing actionable insights from complex data and designing experiments to measure impact. Highlight your ability to define metrics, control for confounding factors, and communicate results.

3.2.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe how you’d design an experiment or A/B test, select key metrics (e.g., conversion, retention, revenue), and analyze short- and long-term effects.

Example answer: "I’d run a controlled experiment, tracking ride volume, customer retention, and total revenue. I’d compare cohorts before and after the discount, adjusting for seasonality and external factors."

3.2.2 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you’d structure an A/B test, define success criteria, and interpret statistical significance.

Example answer: "I’d split users randomly, define a clear primary metric, and use hypothesis testing to assess impact. I’d ensure sample sizes are adequate and report confidence intervals for transparency."

3.2.3 How would you estimate the number of gas stations in the US without direct data?
Discuss your approach to solving estimation problems using proxy variables, external datasets, and logical reasoning.

Example answer: "I’d use population density and average gas stations per capita in sample regions, then extrapolate nationally. I’d validate my estimate with industry reports or government statistics."

3.2.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Outline your process for profiling, cleaning, joining, and analyzing heterogeneous data sources. Address challenges like schema mismatches and data quality.

Example answer: "I’d profile each dataset, resolve schema conflicts, and use keys like user IDs to join data. I’d apply normalization and outlier detection, then build dashboards or models to surface actionable insights."

3.3 Data Engineering & Pipeline Design

These questions assess your experience in building scalable, reliable data infrastructure and optimizing ETL processes. Focus on practical solutions for handling large volumes and ensuring data quality.

3.3.1 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe how you’d design a robust ETL pipeline, ensure data integrity, and enable timely analytics.

Example answer: "I’d automate data ingestion with scheduled jobs, validate schema integrity, and implement error logging. I’d partition data for efficient querying and track lineage for compliance."

3.3.2 Ensuring data quality within a complex ETL setup
Explain methods for monitoring, alerting, and remediating data quality issues in multi-source ETL environments.

Example answer: "I’d implement automated checks for duplicates, missing values, and outlier detection. Regular audits and cross-team syncs would address discrepancies quickly."

3.3.3 Prioritized debt reduction, process improvement, and a focus on maintainability for fintech efficiency
Discuss strategies for identifying and reducing technical debt in analytics pipelines, including refactoring, documentation, and automation.

Example answer: "I’d inventory pipeline bottlenecks, refactor legacy code, and introduce automated testing. I’d prioritize fixes based on business impact and maintain clear documentation."

3.3.4 Modifying a billion rows
Describe approaches for efficiently updating or processing massive datasets, including batching, indexing, and distributed computing.

Example answer: "I’d use bulk updates with batching, leverage indexes for speed, and, if needed, parallelize operations using cloud infrastructure."

3.4 Communication & Stakeholder Management

Expect questions that evaluate your ability to present insights, tailor communication, and manage stakeholder expectations. Emphasize clarity, adaptability, and business acumen.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss techniques for translating technical results into business value, adjusting your message for different audiences.

Example answer: "I’d use visualizations and analogies to simplify findings, highlight actionable recommendations, and adjust depth based on stakeholder expertise."

3.4.2 Demystifying data for non-technical users through visualization and clear communication
Explain how you make data accessible, including dashboard design and storytelling.

Example answer: "I’d design intuitive dashboards, use clear labeling, and provide context with business-relevant examples to engage non-technical stakeholders."

3.4.3 Making data-driven insights actionable for those without technical expertise
Describe your approach to distilling complex analyses into concrete recommendations.

Example answer: "I’d break down findings into step-by-step actions, relate metrics to business goals, and anticipate follow-up questions to ensure adoption."

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Share your process for aligning goals, negotiating scope, and keeping communication open.

Example answer: "I’d clarify objectives early, use prioritization frameworks, and maintain a change log to ensure all parties are informed and aligned."

3.5 SQL & Data Manipulation

These questions test your ability to query, aggregate, and transform data efficiently. Demonstrate your fluency in SQL and your attention to data quality and business relevance.

3.5.1 Write a SQL query to count transactions filtered by several criterias.
Explain how you’d structure the query, apply appropriate filters, and ensure accuracy.

Example answer: "I’d use WHERE clauses for each criterion and aggregate with COUNT(*). I’d validate edge cases and ensure indexes support the query for performance."

3.5.2 Calculate total and average expenses for each department.
Describe your approach to grouping, aggregating, and presenting results.

Example answer: "I’d group by department, use SUM and AVG for expenses, and format the output for easy executive review."

3.5.3 Write a SQL query to compute the median household income for each city
Summarize how to calculate medians using window functions or subqueries.

Example answer: "I’d partition by city, order incomes, and select the middle value using window functions or percentiles."

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3.6 Behavioral Questions

3.6.1 Tell Me About a Time You Used Data to Make a Decision
Share a specific example where your analysis led to a measurable business outcome. Focus on the decision-making process and the impact.

3.6.2 Describe a Challenging Data Project and How You Handled It
Highlight a project with technical or organizational hurdles, your approach to overcoming them, and the lessons learned.

3.6.3 How Do You Handle Unclear Requirements or Ambiguity?
Explain your strategy for clarifying goals, collaborating with stakeholders, and iterating on solutions when requirements are not well-defined.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Discuss your communication and negotiation skills, and how you fostered alignment.

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Illustrate your prioritization framework and communication loop to manage expectations and maintain project integrity.

3.6.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly
Show your commitment to quality while delivering value under tight deadlines.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation
Describe how you built trust, presented evidence, and drove consensus.

3.6.8 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Share your triage process for rapid data cleaning and transparent communication of limitations.

3.6.9 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to missing data, the methods used, and how you communicated uncertainty.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again
Describe the tools and processes you implemented for ongoing data reliability.

4.1 Company-specific tips:

Familiarize yourself with the mortgage lending industry and New American Funding’s specific business model. Dive into how data science is transforming home loan origination, risk assessment, and customer experience in financial services. Read up on recent industry trends, regulatory changes, and how technology is shaping consumer lending. Understanding these broader themes will help you contextualize your technical answers and demonstrate business acumen.

Research New American Funding’s core products, such as home loans, refinancing options, and their in-house loan processing systems. Be prepared to discuss how data science can optimize these offerings—think predictive analytics for loan approvals, fraud detection, and customer segmentation. Reference recent company initiatives or awards to show genuine interest and alignment with their mission.

Reflect on how compliance and data privacy impact analytics in the mortgage industry. New American Funding operates in a highly regulated environment, so show awareness of how you would handle sensitive financial information, ensure data integrity, and support regulatory reporting. This will position you as a candidate who understands the stakes and can deliver trustworthy solutions.

4.2 Role-specific tips:

Develop expertise in cleaning and integrating heterogeneous financial datasets.
Practice profiling, cleaning, and joining data from multiple sources such as payment transactions, user behavior logs, and external credit reports. Emphasize your ability to resolve schema mismatches, handle missing values, and ensure data quality—especially when deadlines are tight and insights must drive immediate business decisions.

Sharpen your skills in designing and validating predictive models for risk and customer behavior.
Focus on building models relevant to lending, such as credit scoring, default prediction, and churn analysis. Be ready to discuss your approach to feature engineering, handling class imbalance, and selecting evaluation metrics that align with business goals. Prepare to articulate the trade-offs between accuracy, interpretability, and regulatory compliance.

Prepare to communicate complex analyses to non-technical stakeholders.
Practice translating technical findings into actionable recommendations for business, operations, and leadership teams. Use clear visuals, analogies, and business-relevant examples to make your insights accessible. Anticipate follow-up questions and be ready to adjust your message based on audience expertise.

Demonstrate your ability to design robust, scalable ETL pipelines for financial data.
Review best practices for automating data ingestion, validating schema integrity, and tracking lineage for compliance. Highlight your experience with partitioning, bulk updates, and distributed computing for processing large datasets—showing you can support both analytics and regulatory needs.

Showcase your SQL fluency for business-critical queries.
Practice writing queries that aggregate, filter, and transform financial data—such as counting approved transactions, calculating average loan amounts by segment, or computing median household income by region. Emphasize both correctness and performance, and be ready to explain your approach to handling edge cases and optimizing queries.

Prepare concise STAR-format stories for behavioral interviews.
Develop examples that highlight your impact in cross-functional projects, navigating ambiguity, and resolving stakeholder conflicts. Be specific about how you balanced speed with data integrity, influenced business decisions, and automated processes to prevent future data-quality issues. This will demonstrate your adaptability and leadership in a fast-paced, regulated environment.

Demonstrate your commitment to data privacy and regulatory compliance.
Be ready to discuss how you protect sensitive financial information, implement access controls, and ensure your models and pipelines support auditability. Reference relevant laws or standards (like GDPR or CCPA) if appropriate, and explain how compliance shapes your workflow as a data scientist in financial services.

Show your ability to drive process improvement and reduce technical debt.
Highlight your experience identifying pipeline bottlenecks, refactoring legacy code, and automating data-quality checks. Explain how these improvements supported business growth, reduced risk, and ensured ongoing reliability in data-driven decision-making.

Practice rapid data triage for urgent business needs.
Prepare to share your approach to quickly cleaning and analyzing messy datasets under tight deadlines. Emphasize transparent communication of limitations and trade-offs, and show how you deliver critical insights even when data quality is imperfect.

Be ready to present and defend your analytical decisions.
Expect to be challenged on your modeling choices, experimental design, and recommendations. Practice articulating your reasoning, considering alternative approaches, and responding confidently to feedback from technical and non-technical interviewers alike.

5.1 How hard is the New American Funding Data Scientist interview?
The New American Funding Data Scientist interview is moderately to highly challenging, especially for candidates new to the financial services sector. Expect a blend of technical rigor—covering machine learning, data analysis, SQL, and pipeline design—and a strong emphasis on business problem-solving and stakeholder communication. The process tests your ability to translate complex data into actionable insights in a fast-paced, regulated environment.

5.2 How many interview rounds does New American Funding have for Data Scientist?
Typically, there are 5-6 interview rounds: an initial recruiter screen, one or two technical/case interviews, a behavioral interview, and a final onsite or virtual round with multiple team members. Some candidates may also be asked to complete a take-home assignment or technical assessment.

5.3 Does New American Funding ask for take-home assignments for Data Scientist?
Yes, take-home assignments are common for the Data Scientist role. These usually involve analyzing a business-relevant dataset, building a predictive model, or solving an open-ended analytics problem. The assignment is designed to assess your technical skills, problem-solving approach, and ability to communicate results clearly.

5.4 What skills are required for the New American Funding Data Scientist?
Key skills include advanced proficiency in Python and SQL, experience with machine learning and statistical modeling, data cleaning and integration, and the ability to communicate complex analyses to non-technical stakeholders. Familiarity with financial services data, regulatory compliance, and building scalable ETL pipelines is highly valued.

5.5 How long does the New American Funding Data Scientist hiring process take?
The typical timeline is 3 to 5 weeks from initial application to offer. Fast-track candidates with highly relevant experience may complete the process in 2-3 weeks, while scheduling complexities or additional assessment rounds can extend the timeline slightly.

5.6 What types of questions are asked in the New American Funding Data Scientist interview?
Expect a mix of technical questions (machine learning, data analysis, SQL, and data engineering), business case studies, and behavioral scenarios. You’ll be asked to design predictive models, analyze financial datasets, solve estimation problems, and discuss your approach to stakeholder management and communication.

5.7 Does New American Funding give feedback after the Data Scientist interview?
New American Funding typically provides high-level feedback through recruiters. While you may receive general insights about your interview performance, detailed technical feedback is less common, especially for candidates who do not advance past the final rounds.

5.8 What is the acceptance rate for New American Funding Data Scientist applicants?
While exact acceptance rates are not published, the Data Scientist role at New American Funding is competitive. An estimated 3-5% of qualified applicants receive offers, reflecting the company’s high standards for technical expertise and business acumen in the financial sector.

5.9 Does New American Funding hire remote Data Scientist positions?
Yes, New American Funding offers remote Data Scientist positions, with some roles requiring occasional visits to the office for team collaboration or project kickoffs. The company supports flexible work arrangements to attract top talent nationwide.

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