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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?

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

Write a query to randomly sample a row from a big table.

Random SQL Sample

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Blend is seeking experienced Data Scientists to join their team for a critical project within the Supply Chain domain. The successful candidates will be responsible for leveraging Dataiku for data processing and machine learning tasks. This is an excellent opportunity for professionals with a strong background in Dataiku, data science, and machine learning to make a significant impact on Diageo's supply chain processes. Key Responsibilities: <ul><li>Utilize Dataiku for data processing, model building, and deployment of machine learning solutions. Develop and implement supervised learning models, optimization algorithms, and hypothesis testing procedures to improve supply chain operations.</li><li>Collaborate with cross-functional teams to integrate data science solutions into the supply chain workflow.</li><li>Ensure the robustness and scalability of machine learning models in a production-grade environment.</li><li>Work with Databricks for data engineering tasks and pipeline management.</li><li>Apply MLOps processes to streamline model deployment and monitoring.</li></ul> Required Skills and Experience: <ul><li>Dataiku: Minimum 5+ years of solid experience with Dataiku, including data preparation, model building, and deployment.</li><li>Data Science and Machine Learning: Proven hands-on experience in data science, machine learning, and statistics. Strong focus on supervised learning, optimization techniques, and hypothesis testing.</li><li>Python: Proficiency in Python for data analysis, model development, and scripting.</li><li>Databricks: Experience with Databricks for managing data pipelines and performing data engineering tasks.</li><li>MLOps: Familiarity with MLOps processes to support the deployment and monitoring of machine learning models in production</li></ul> Qualifications: <ul><li>Bachelor's or master's degree in data science, Computer Science, Statistics, or a related field and strong analytical and problem-solving skills.</li></ul>

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