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Howmet Aerospace Interview Questions

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Machine Learning

Bank Fraud Model

Lasso vs Ridge

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

Bagging vs Boosting

Loan Model

Analytics

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

How would you approach improving the quality of airline data?

Addressing Data Quality Issues

Let's say you work at Facebook and you're analyzing churn on the platform.

Retention Rate Disparity

Data Structures & Algorithms

Given a singly linked list, reverse the list starting from index k.

Reverse List Starting at Index K

Write a function that returns the number of triplets in the array that sum to `k`.

Triplet Counting

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Statistics

Covariance vs Correlation

What does it mean to "bootstrap" a data set?

Bootstrapping Samples

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

Describe a data project you worked on. What were some of the challenges you faced?

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

Classification and Regression

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

How would you explain the bias variance tradeoff in machine learning to a high school student?

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

Let’s say that you’re a data scientist at DoorDash.

How would you build a model to predict which merchants the company should go after for acquisition when entering a new market?

How to model merchant acquisition in a new market?

Merchant Acquisition

Let’s say you work as a data scientist at DoorDash. 

You are tasked to build a machine learning system that minimizes missing or wrong orders placed on the app.

How would you go about designing this system?

Minimize Wrong Orders

What does it mean to “bootstrap” a data set?

Give an example of a time when it would be appropriate to bootstrap a smaller sample(s) from a larger data set.

What are the advantages and disadvantages of doing so?

Let’s say that you work for a company like Netflix. Netflix has two pricing plans: $15/month or $100/year.

Let’s say an executive at the company wants you to analyze the churn behavior of users that subscribe to either plan.

What kinds of metrics / graphs / models would you build to help give the executive an over-arching view of how the subscriptions are performing?

What is the difference between covariance and correlation? Provide an example.

Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

<ol>
<li>What kind of model did the co-worker develop? 
</li>
<li>Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe? 
</li>
<li>What metrics would you track to measure the success of the new model?</li>
</ol>

What is the Martingale strategy and how might it be used in online advertising?

The use of Martingale strategy for finance and online advertising

Martingale Strategy

Probability

Imagine you run a pizza franchise, and you run into a problem with a lot of no-shows after customers place their order.

What features would you include in a model to try to predict a no-show?

Pizza No Show

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

Risk Model for a Mortgage Bank

Business Case

Suppose we are training a binary classification algorithm. 

The outcome is imbalanced, with 99.8% of individuals in our sample having an outcome value of 0, and 0.2% having an outcome value of 1. To build a model, we down-sample the training data using a random sample of 1% of the individuals who have an outcome of 0 and keeping all individuals with an outcome of 1.

Now we train the model on the smaller sample and build a binary classifier that predicts the probability of an individual with a value of 1.

How would we then adjust our output probabilities to use this model on the total, imbalanced population?

Let’s say you’re trying to calculate churn for a subscription product.

You noticed that of all customers that bought subscriptions in January 2020, about 10% of them canceled their membership before their next cycle on February 1st.  

If you assume that your new customer acquisition is uniform throughout each month and that customer churn goes down by 20% month over month, what’s the expected churn rate in March for all customers that bought the product since January 1st?

Expected Churn

Product Sense & Metrics

You’re given the head node of a singly linked list and a natural number <code>k</code>. <code>k</code> will always be less than or equal to the length of the list, such that <code>k &lt;= len(SinglyLinkedList)</code>. Write a function <code>reverse_at_position(head, position)</code> that reverses the linked list starting from the index <code>k</code> (the index starts at 0) to the end of the list.

A class <code>Node</code> is defined as the following:

<pre tabindex="0" class="chroma"><code>class Node:
 def __init__(self, element, next=None):
 self.element = element
 self.next = next
</code></pre>

Note: After the reverse operations, you must update the new head node using the <code>update_head(node)</code> function (especially in the case where <code>n = 1</code>). The tail’s next node will be <code>None</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>reverse_at_position([1, 2, 3, 4, 5, 6], 2)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[1, 2, 3, 6, 5, 4]
</code></pre>

Imagine you have been given an array of integers, and a query number <code>k</code>. Your task is to write a function that finds all the triplets in the array that sum up to the query number <code>k</code>.

Let’s illustrate this with some examples:

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 4, 5]
k = 6
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 1
</code></pre>

In this case, there is only one triplet that sums up to 6: (1, 2, 3).

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 10, 20, 30, 40]
k = 60
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>count_triplets(nums, k) -&gt; 3
</code></pre>

In this case, there are three triplets that sum up to 60: (10, 10, 40), (10, 20, 30), and (10, 20, 30).
The value 10 can be retrieved as <code>nums[0]</code> or <code>nums[1]</code>, therefore resulting in two triplets with the same value.

Let’s say you’re a senior data analyst and your team is tasked with addressing data quality issues for an airline company. Customer records, booking details, and flight schedules often have missing fields, duplicate entries, or incorrect data.

How would you approach improving the quality of this data and ensuring it remains clean and reliable over time?

Let’s say you work at Facebook and you’re analyzing churn on the platform.

<ol>
<li>How would you investigate if a disparity exists in retention on different Facebook products?</li>

<li>How would you investigate the causes of such a disparity?</li>
</ol>

Imagine you are trying to track the position of a moving car using noisy GPS data, how would you explain the Kalman filter to someone with no technical background?

Use a real-world analogy to make your explanation accessible.

Explain the Kalman filter in simple, real-world terms.

Kalman Filter in GPS tracking

Forecasting & Time Series

Challenge

Sr. Engineering ManagerJob Roles:* Accountability and follow-through--interacts within organizational matrix understanding the inherent interdependence of roles and functions; takes ownership of outcomes; acts in the best interest of the team, group, function and enterprise.* Rigor--continuous focus on repeatable processes at scale; analyzes efforts; recalibrates as needed; considers multiple inputs from multiple stakeholders; exhibits detail orientation.* Tactical agility--inspires team to proactive improvements while maintaining ability to react to issues as they arise; functions well under pressure of production and time expectations; able to manage the tension of speed and specification demands.* Practical and adaptive thinking--understands organizational capabilities and introduces solutions within existing frameworks while simultaneously adapting to changes, developments in technology and marketplace demands.* Versatile leadership--technically capable, yet able to detach to gain perspective; deploys resources strategically and works alongside team when necessary.* Possibility thinking--proactively challenges the status quo and seeks to solve problems differently than we have in the past; explores the boundaries of technology and current practice; embraces and champions change including automation, smart manufacturing and progress in data gathering and interpretation.* Relationship-oriented--establishes and maintains connections with team, peers and cross functionally; collaborates and compromises for the good of the enterprise; provides thought partnership; creates and scales best practices across enterprise.* Prioritization--understands the business and its technical, financial and strategic direction and goals; prioritizes work accordingly and communicates rationale for decisions; advocates for customers within the context of organizational capability and goals; understands the implication of decisions on other facets of the enterprise.* Communication--listens well and takes multiple perspectives into consideration; articulates priorities and future possibilities clearly and frequently; interacts with operators, managers and customers and customizes approach to each stakeholder group.* Coach/Mentor/Manager-- formally and informally develops others and contributes to talent factory strategy; provides direction, review, reassurance, encouragement, challenge, recognition on an individual basis; promotes growth and development in associates; inspires risk; continually assesses and reassesses talent and fit to role based on individual and collective performance.* Resource and expertise deployment--understands where to invest to ensure resolution; solicits and directs the expertise and involvement of others and enables their success.* Customer coordination--responds directly to customer inquiries, interfaces with customer.* Influence without direct authority--provides justification for decisions and direction; presents a compelling case for needed efforts or change to parties with varying interests and priorities; aligns peers and stakeholders to achieve both tangible and anticipated objectives*Basic Qualifications:* Bachelor's degree in engineering from an accredited institution.* Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.* This position is subject to the International Traffic in Arms Regulations (ITAR) which requires U.S. person status. ITAR defines U.S. person as an U.S. Citizen, U.S. Permanent Resident (i.e. 'Green Card Holder'), Political Asylee, or Refugee.Preferred Qualifications:* 10+yrs of Engineering Leadership or related technical experience.* Strong Technical background in Manufacturing/Process, Tooling, Machining,* Strong analytical & strategic approach.* Bilingual English Spanish.* Understands Mxico/US bilateral culture & Manufacturing environment.* Professional Engineer, ISO/AS9100, and NADCAP certifications.* Strong culture of Talent & succession management.* High level of mentorship & team collaboration approach.* High Level of PC skills, including a high proficiency in Microsoft Office package.* High Level of written & verbal communications skills.* Positive attitude and initiative to develop the position's activities.Take advantage to join a great workplace and develop all your full potential and grow inside our Aerospace Company!Interested candidates please:1.-Apply to posting on below link:Howmet Aerospace Careers2.-And/or send email to: ...@howmet.com3.-Leave physical resume at 2022 Cienegas Rd. Del Rio Tx on business hours.EOE, including Disability/VetsJob Responsibilities:* The position is dedicated to the technical leadership of the Process/Product Engineering & Technical Tooling Team. The position is additionally responsible for the technical support of manufacturing focused on providing a consistent product that meets customer specifications with additional responsibilities including, but not limited to:* Lead initiatives to achieve product yield improvements, variation reduction, and achieve cost reduction goals related to the manufacture of ceramic cores/parts.* Direct and manage the activities regarding product tool/part designs and standards* Manage the facility's engineering budgets.* Manage and direct the New Product Introduction (NPI) initiative ensuring product development in accordance with overall project timelines, budget, and quoted standards* Develop the overall plant scrap reduction plan and manage the initiatives and actions to meet the scrap targets.* Work in conjunction with the Process Engineering Manager to achieve all plant financial and scrap reduction metrics.* Report status of scrap reduction initiatives to Plant Manager and other high-level managers as required.* Direct and participate in the conception, evaluation and implementation of cost improvement, product improvement and production problem solving activities.* Coordinate scrap reduction initiatives with the HRC and Corporate Engineering technical staff* Establish professional working relationship with Engineering Managers and Engineering Team at each casting facility and promote engineering team communication regarding product status and issues.* Work directly with engine manufacturer's engineering team in conjunction with the casting facility's engineering team to optimize core design and producibility as required.* Responsible for developing the future strategic engineering organizational structure with the Process Engineering Manager, the Plant Manager, and the HR manager.* Responsible for creating an environment of engagement and fostering development of Engineering Staff* Manage training development strategies for the Engineering Staff.

<div><h2>Commercial Sales Specialist</h2>Maintain sales and provide support for all sales efforts by effectively guiding the Customer and Sales Team through the Volume Quotation Process, including but not limited to those listed below. To be a proactive part of the Sales and Customer Service Team by demonstrating leadership skills through the Quotation Process. To understand Fastening Systems, their technical characteristics, suitable applications, Cost Analysis and Pricing Programs to complete Volume Quotations and assist Sales and Customers in determining the best product for their individual requirements.Responsibilities:<ul><li>Respond to verbal and written requests for Volume Quotation with price, delivery and product information.</li><li>Complete profit analysis and help manage the Quotation Guidelines for Volume Quotation requests.</li><li>Work with Planners and Manufacturing from "True Cost" for proper cost and DVP analysis, and ensure that quotes adhere to DVP guideline (with guidance from Management).</li><li>Ability to use product knowledge, availability and customer need to best fit their application requirements.</li><li>Communicate Quotations with Customer and Sales at all levels and disseminate information for positive results.</li><li>Check stock and availability to communicate best options for the customer via the Quotation process.</li><li>Submit Quotations to the Sales Team and Customer for proper follow up and tracking of results.</li><li>Maintain, update and distribute all price lists internally and externally to the customers, including tariff recovery.</li><li>Manage the Quotation communication to maximize visibility for efficient buying patterns to reduce backorders.</li><li>Streamline and simplify existing price schedules where possible, with guidance from Management.</li><li>Help administer Blanket Order and Special Price Agreement profit analysis, and coordinate with Business Development Manager for final review and Sales Management approvals.</li><li>Provide feedback to Management on Market changes and opportunities for process improvement.</li><li>Ensures that all work activities are performed in accordance with established safety and environmental standards.</li><li>Maintains full compliance with Company policies and procedures.</li><li>Demonstrates a knowledge of and commitment to lean manufacturing principles and techniques.</li><li>5+ years of Quotation and Profit Analysis with Volume Quote request in like industry within an Industrial products environment showing a proven track record of success.</li><li>High school diploma required; Bachelor's degree preferred.</li><li>Strong interpersonal and team skills to build positive relationships with people at all levels.</li><li>Ability to work independently in fast-paced, goal oriented environment.</li><li>Organizational skills to complete a variety of tasks and assignments in an effective manner.</li><li>Working knowledge of Microsoft Excel, Word and PowerPoint plus good internet skills.</li><li>Must be able to read, write in Standard English and perform mathematical calculations.</li><li>Problem solving skills to arrive at reliable conclusions based on analysis of factual information.</li><li>Ability to gain thorough knowledge of industrial fastening systems and their applications.</li><li>Strong desire and ability to create positive results in a winning environment.</li></ul>The above statements are intended to describe the general nature and level of work being performed. They are not intended to be an exhaustive list of all responsibilities, duties and skills required of incumbents in this job classification.Howmet Aerospace Inc. (NYSE: HWM), headquartered in Pittsburgh, Pennsylvania, is a leading global provider of advanced engineered solutions for the aerospace and transportation industries. Our primary businesses focus on jet engine components, aerospace fastening systems, titanium structural parts and forged wheels. With $7.4 Billion in revenue in 2024, our products play a crucial role in enabling fuel efficiency and lightweighting, contributing to our customers' success and making a positive impact on the world.Howmet is proud to be an Equal Employment Opportunity employer. We are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or other applicable legally protected characteristics. If you need assistance to complete your application due to a disability, please email TalentAcquisitionCoE_Howmet@howmet.com.</div>

<div><h2>Engineering Position</h2>The position is dedicated to the technical leadership of the Process Engineering Team. The position is additionally responsible for the technical support of manufacturing focused on providing a consistent product that meets customer specifications with additional responsibilities including, but not limited to:<ul><li>Lead initiatives to achieve product yield improvements, variation reduction, and achieve cost reduction goals related to the manufacture of ceramic cores/parts.</li><li>Direct and manage the activities regarding process standards and work instructions.</li><li>Manage the facility's engineering budgets.</li><li>Support New Product Introduction (NPI) initiatives ensuring product development in accordance with overall project timelines, budget, and quoted standards.</li><li>Develop the overall plant scrap reduction plan and manage the initiatives and actions to meet the scrap targets.</li><li>Work in conjunction with the Product Engineering Manager to achieve all plant financial and scrap reduction metrics.</li><li>Report status of scrap reduction initiatives to Plant Manager and other high-level managers as required.</li><li>Direct and participate in the conception, evaluation and implementation of cost improvement, product improvement and production problem solving activities.</li><li>Coordinate scrap reduction initiatives with the HRC and Corporate Engineering technical staff</li><li>Establish professional working relationship with Engineering Managers and Engineering Team at each casting facility and promote engineering team communication regarding product status and issues.</li><li>Establish professional working relationship with HRC, suppliers of raw materials, equipment, and services and promote engineering team communication regarding supply status, material, quality, testing and issues.</li><li>Work directly with process customer's engineering team in conjunction with the casting facility's engineering team to optimize materials, core design, and producibility as required.</li><li>Responsible for developing the future strategic engineering organizational structure with the Product and Tooling Engineering Manager, the Plant Manager, and the HR manager.</li><li>Responsible for creating an environment of engagement and fostering development of Engineering Staff</li><li>Manage training development strategies for the Engineering Staff</li><li>Maintain high attendance record.</li></ul>Job Roles:<ul><li>Accountability and follow-through--interacts within organizational matrix understanding the inherent interdependence of roles and functions; takes ownership of outcomes; acts in the best interest of the team, group, function, and enterprise.</li><li>Rigor--continuous focus on repeatable processes at scale; analyzes efforts; recalibrates as needed; considers multiple inputs from multiple stakeholders; exhibits detail orientation.</li><li>Tactical agility--inspires team to proactive improvements while maintaining ability to react to issues as they arise; functions well under pressure of production and time expectations; able to manage the tension of speed and specification demands.</li><li>Practical and adaptive thinking--understands organizational capabilities and introduces solutions within existing frameworks while simultaneously adapting to changes, developments in technology and marketplace demands.</li><li>Versatile leadership--technically capable, yet able to detach to gain perspective; deploys resources strategically and works alongside team when necessary.</li><li>Possibility thinking--proactively challenges the status quo and seeks to solve problems differently than we have in the past; explores the boundaries of technology and current practice; embraces and champions change including automation, smart manufacturing and progress in data gathering and interpretation.</li><li>Relationship-oriented--establishes and maintains connections with team, peers and cross functionally; collaborates and compromises for the good of the enterprise; provides thought partnership; creates and scales best practices across enterprise.</li><li>Prioritization--understands the business and its technical, financial, and strategic direction and goals; prioritizes work accordingly and communicates rationale for decisions; advocates for customers within the context of organizational capability and goals; understands the implication of decisions on other facets of the enterprise.</li><li>Communication--listens well and takes multiple perspectives into consideration; articulates priorities and future possibilities clearly and frequently; interacts with operators, managers and customers and customizes approach to each stakeholder group.</li><li>Coach/Mentor/Manager--provides direction, review, reassurance, encouragement, challenge, recognition on an individual basis; promotes growth and development in associates; inspires risk; continually assesses and reassesses talent and fit to role based on individual and collective performance.</li><li>Resource and expertise deployment--understands where to invest to ensure resolution; solicits and directs the expertise and involvement of others and enables their success.</li></ul><h2>Qualifications</h2><ul><li>Bachelor's Degree in the field of Chemical or Material Science Engineering.</li><li>8+ years in engineering field and 3+ years of supervisory experience</li><li>Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.</li></ul>ITAR<ul><li>This position entails access to export-controlled items and employment offers are conditioned upon an applicant's ability to lawfully obtain access to such items.</li></ul><h2>About Us</h2>Howmet Aerospace Inc. (NYSE: HWM), headquartered in Pittsburgh, Pennsylvania, is a leading global provider of advanced engineered solutions for the aerospace and transportation industries. Our primary businesses focus on jet engine components, aerospace fastening systems, titanium structural parts and forged wheels. With $7.4 Billion in revenue in 2024, our products play a crucial role in enabling fuel efficiency and lightweighting, contributing to our customers' success and making a positive impact on the world.</div>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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