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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 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

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Role: Data Science Manager Location · Anywhere, Florida (Remote) Full Time  Job Description This is an exciting and rare opportunity to join a small yet well-capitalized company, on the ground floor, and prepare for a significant, aggressive scale over the next 2-3 years. The company is a FinTech company with a startup feel, looking to derive business value through the adoption of advanced predictive models including Artificial Intelligence and Machine Learning paired with a unique seamless customer experience. The company has been providing working capital to small businesses in Canada and the USA for over 10 years. They are applying predictive technology to large volumes of data and against a wide spectrum of business and owner data to make fast and accurate credit decisions. Speed, accuracy, and automation are causing the company to grow exponentially.  A successful candidate will be a person who is innovative and will further differentiate financial products on the market to create more stickiness. The candidate enjoys working with many departments and wearing many hats as this person will be responsible for various projects inside the organization working with the Leadership team. With a “Growth Mindset” and Inclusive Team Culture, the company embraces differences. They are committed to furthering the culture of personal and professional growth while producing bottom line results.  Qualifications: <ul><li>10+ years of industry experience in predictive & algorithmic modeling, data science, and data analysis. </li><li>Experience with ML fields, e.g., predictive analytics and financial modeling, statistical learning theory. </li><li>Experience with maintaining and monitoring ML models in a production environment </li><li>Expertise in modelling, analysis & building systems using Python. </li><li>Experience in diving into data to discover hidden patterns, using data visualization tools, writing SQL, and working with modern tools to develop models </li><li>Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations </li><li>Team player, open minded, motivated and dedicated </li><li>Strong communications with team members, ability to lead and mentor </li></ul> Responsibilities/Competencies: <ul><li>Collaborate with business domain subject matter experts in various departments to understand the problem and the objectives. </li><li>Ability to translate complex data into actionable insights for a non-technical audience. </li><li>Research and build innovative predictive statistical models for all areas of the business </li><li>Provide mentorship to junior data scientists in aspects of machine learning, data and software engineering, and assist them with practical guidance, when needed </li><li>Maintain, tune, improve model behavior with live data for the company’s credit decision model for lending operations to drive profitability. </li><li>Entrepreneurial mindset </li><li>Highly Organized </li><li>Dynamic and able to handle many moving pieces at once. </li><li>Strong teaching and mentoring skills for the purpose of building up talent and delegating responsibilities to ensure maximum productivity and grow the team </li><li>Vocal and able to communicate needs inspire </li><li>Fully accountable for the success and failure of the Data Science team and its' people management </li></ul>

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