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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

We’re Hiring: Data Architect @ Emids Technologies Key responsibilities:<ul><li>Design end-to-end data architecture supporting OLTP systems, ODS layers, and downstream data warehouses.</li><li>Architect ODS solutions to serve as a bridge between real-time transactional systems and analytical platforms.</li><li>Develop and optimize relational data models in PostgreSQL and DB2 aligned to business needs.</li><li>Define data modeling standards, schema designs, and partitioning/indexing strategies for transactional and ODS workloads.</li><li>Implement & maintain data dictionary , ensure data lineage & traceability.</li><li>Ensure data consistency, normalization, and integrity across heterogeneous systems.</li><li>Collaborate with business, data engineering, and analytics teams to align OLTP and ODS architecture.</li></ul> Required skills:<ul><li>Experience in data architecture across OLTP and analytical systems.</li><li>Deep hands-on expertise in AWS Aurora PostgreSQL and RDS PostgreSQL.</li><li>Deep hands-on experience in AWS IAM, PostgreSQL role, and setup integration between the both.</li><li>Understanding with IBM DB2 (LUW or z/OS) including schema design.</li><li>Proven experience designing and implementing ODS and data warehouses (Snowflake, Redshift, etc.).</li><li>Strong SQL, ETL architecture, and data integration experience.</li><li>Familiarity with data warehousing principles, slowly changing dimensions, and dimensional modeling (Kimball/Inmon).</li><li>Hands-on with AWS data services: DMS, Glue, S3, Lambda, CloudWatch.</li><li>Knowledge of data governance, data quality, and compliance practices.</li></ul>

We are looking for a highly skilled Data Engineer with strong expertise in AWS, Databricks, PySpark, and Airflow to join our growing Data Engineering team. The ideal candidate will be responsible for designing, building, and optimizing scalable data pipelines and solutions that enable advanced analytics, machine learning, and business intelligence across the organization. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and Airflow. Build and optimize data models and data lakes/warehouses on AWS Implement best practices for data quality, data governance, and performance optimization. Collaborate with cross-functional teams (data scientists, analysts, product, and business teams) to deliver data-driven solutions. Ensure reliability, scalability, and efficiency of data workflows through automation and monitoring. Troubleshoot complex data engineering issues and optimize processing performance. Required Skills & Qualifications Bachelor’s/Master’s degree in Computer Science, Engineering, or related field. 4+ years of hands-on experience in Data Engineering. Strong expertise in PySpark, Databricks, and Airflow for large-scale data processing and orchestration. Solid experience with AWS services such as S3, Glue, Redshift, EMR, Lambda, and IAM. Strong knowledge of SQL and performance tuning. Experience with CI/CD pipelines, Git, and containerization (Docker/Kubernetes) is a plus. Strong problem-solving skills, communication, and ability to work in a fast-paced environment.

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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