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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?

In an A/B test, how can you check if assignment to the various buckets was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

Random Bucketing

A/B Testing & Experimentation

???? Now Hiring: Senior Data Analyst – Digital AI (Local to DFW Only)???? Location: Irving, TX – 100% Onsite Role???? Immediate Need | High-Visibility AI/Data Role | Local Candidates Strongly Preferred Digital Product Management team is expanding, and we’re looking for a Senior AI Data Analyst to join our Digital organization right here in the Dallas–Fort Worth metroplex.If you’re passionate about using data to fuel next-generation AI experiences—and you enjoy solving problems at peta-byte scale—this is your chance to work on real, high-impact initiatives used by millions of customers daily.???? What You’ll DoIn this role, you will play a key part in shaping the performance of our Machine Learning systems. You’ll work hands-on with large datasets inside Databricks, generate meaningful insights, and partner closely with cross-functional teams to optimize AI models and digital products across our ecosystem.You will:<ul><li>Research, prototype, and build insights & visualizations for ML pipelines</li><li>Analyze production data to uncover actionable trends and improve model performance</li><li>Keep up with emerging tech—new libraries, tools, and frameworks</li><li>Collaborate with experts across ML, computer vision, and systems engineering</li><li>Deliver high-quality research and analysis on fast-paced timelines</li></ul>???? What You Bring<ul><li>5+ years of experience applying statistical techniques, A/B testing, KPI design, segmentation, and experimentation</li><li>3+ years hands-on in the Databricks ecosystem (Azure preferred)</li><li>4+ years manipulating big data using Python, PySpark, and/or SQL</li><li>Strong experience with visualization tools (Python viz libraries, Power BI, etc.)</li><li>A Bachelor’s degree in Computer Science, Engineering, Math, or equivalent</li></ul>Bonus points for:<ul><li>Master’s degree or higher</li><li>Experience evaluating and monitoring ML models</li><li>Ability to work with product/business teams to define KPIs and structure ambiguous problems</li></ul>???? Why Join Us?<ul><li>Work at one of the most recognized brands in the world</li><li>Contribute directly to AI-driven digital experiences used nationwide</li><li>Collaborative team | Cutting-edge data stack | High ownership</li><li>Real impact, not theoretical research</li></ul>???? ImportantWe are looking to fill this role immediately, and preference will be given to candidates already based in DFW who can work in a onsite setup from our Irving office.

Question	Topic	Difficulty	Ask Chance
Boarding Times Bias	Statistics	Medium	Very High
Hundreds of Hypotheses	Statistics	Medium	Very High
Random Bucketing	A/B Testing & Experimentation	Medium	Very High

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