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Daxko Interview Questions

A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

Decreasing Comments

How would you investigate a decline in the average number of comments per user?

<h3>Job Description</h3><div>Job Description<div>Company Description Daxko builds the software that powers fitness, wellness, and community organizations—everything from member management and payments to digital engagement and analytics. From small studios to the largest associations, we help thousands of fitness businesses streamline operations, enhance member experiences, and drive sustainable revenue growth.Job Description We're looking for an AI engineer to help build the core features and pipelines that power our AI platform. You’ll work on retrieval, embeddings, model integrations, and production-grade AI services—turning prototypes into reliable, scalable systems used across multiple products.What You’ll Do<ul><li>Build AI-enabled product features: chat, recommendations, anomaly detection, summarization, workflow automation.</li><li>Contribute to RAG pipelines: ingestion, chunking, embeddings, vector search, retrieval logic.</li><li>Integrate model APIs (OpenAI, Azure OpenAI, AWS Bedrock/SageMaker) into production systems.</li><li>Implement reusable components for prompts, retrieval, and inference routing.</li><li>Write clean, testable, secure code and participate in code reviews.</li><li>Work with QA, DevOps, and Security to ensure reliable deployment and model behavior.</li><li>Translate prototypes into maintainable production services and collaborate with product/UX to embed AI into user workflows.</li><li>Participate in Agile ceremonies and contribute to a culture of high-quality engineering.</li></ul>Qualifications Required: <ul><li>2+ years of software engineering experience.</li><li>Hands-on experience integrating AI APIs (OpenAI, Azure Cognitive Services, AWS Bedrock/SageMaker).</li><li>Strong Python or JavaScript/TypeScript skills (C# a plus).</li><li>Experience with AI/ML frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain.</li><li>Familiarity with embeddings, vector databases, and basic RAG concepts.</li><li>Understanding of microservices, REST/GraphQL APIs, and version control (Git).</li><li>Exposure to cloud environments and CI/CD pipelines.</li><li>Ability to write clear, modular, maintainable code.</li><li>Bachelor’s degree in Computer Science, Data Science, Software Engineering, or related experience </li></ul>Preferred:<ul><li>Master’s degree and/or 3+ years of experience, including at least 1 year focused on AI or ML applications</li><li>Experience with vector databases (Pinecone, Chroma, Elasticsearch, pgvector).</li><li>Experience with prompt engineering, evaluation, and orchestration frameworks (LangChain, LangGraph, LlamaIndex).</li><li>Background in unstructured data processing and embedding pipelines.</li></ul> Additional Information The pay range for this role is $95,700- $160,000 salary per year. Where you fall within the pay range is based on how you demonstrate the attributes and competencies required for the role. We mostly reserve the upper half of our compensation bands for internal growth. In addition to base salary, we offer a comprehensive benefits package, performance-based incentives, and opportunities for growth.#LI-Remote<div></div>Daxko is dedicated to pursuing and hiring a diverse workforce. We are committed to diversity in the broadest sense, including thought and perspective, age, ability, nationality, ethnicity, orientation, and gender. The skills, perspectives, ideas, and experiences of all of our team members contribute to the vitality and success of our purpose and values.We truly care for our team members, and this is reflected through our offices, and benefits, and great perks. These perks are only for our full-time team members. Some of our favorites include:  Flexible paid time off  ⚕️ Affordable health, dental, and vision insurance options   Monthly fitness reimbursement   401(k) matching   New-Parent Paid Leave   Casual work environments   Remote workAll your information will be kept confidential according to EEO guidelines.</div></div>

Question	Topic	Difficulty	Ask Chance
Boarding Times Bias	Statistics	Medium	Very High
Hundreds of Hypotheses	Statistics	Medium	Very High
Decreasing Comments	Product Metrics	Easy	Very High

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