TD Bank is one of the world's leading global financial institutions and ranks as the fifth largest bank in North America by branches. Renowned for delivering exceptional customer experiences to millions of households and businesses, TD Bank operates with over 95,000 talented colleagues across various locations.

The Data Scientist position at TD Bank is a key role within the Data & Analytics line of business. This position requires expertise in SQL, Python, and SAS, among other technical skills. As a Data Scientist, you will engage in data analysis, model development, and predictive analytics to solve complex business challenges and provide business insights. Interview processes typically involve multiple rounds, including technical assessments and behavioral interviews to evaluate your problem-solving skills and cultural fit.

Considering a career at TD Bank as a Data Scientist? This Interview Query guide will help you navigate the interview process and excel in your journey.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining TD Bank as a Data Scientist. Whether you were contacted by a TD Bank recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the TD Bank Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

This initial call will focus on your background and motivation for applying. They may dive into your resume, ask about your previous projects, and inquire about your financial knowledge. The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the TD Bank Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. This stage often consists of:

• Coding/SQL Questions: Expect 2 SQL query questions and one Python algorithm question.
• Statistics and ML Questions: General questions about the end-to-end modeling process, data cleaning, feature engineering, model selection, and performance measurement.
• Financial Questions: Questions on applying data science in the financial domain, such as predicting credit defaults or reviewing and enhancing credit default models.

This stage does not typically involve hands-on coding but will assess your comprehension and problem-solving abilities.

Onsite Interview Rounds

If you advance past the virtual technical interview, you will be invited to attend onsite interview rounds, typically involving:

• Case Presentations: You might be given a take-home assignment to solve and present in front of an AVP and two managers.
• Deep Dive: A deep dive into your resume and past experiences with technical managers and team members.
• Behavioral Interviews: Standard behavioral questions focusing on teamwork, conflict resolution, and motivation.

Throughout the onsite process, your technical prowess, including programming skills, Machine Learning modeling capabilities, and fit with TD Bank’s culture, will be evaluated.

Quick Tips For TD Bank Data Scientist Interviews

• Understand Financial Domain Applications: Familiarize yourself with financial regulations, credit risk models, and other sector-specific applications. TD Bank places significant importance on domain-specific knowledge.
• Practice Technical Skills: Prepare on SQL, Python, SAS, and machine learning algorithms. Focus on end-to-end model-building processes.
• Be Ready for Behavioral Questions: Reflect on your past experiences and how they align with TD Bank’s values and the role requirements. Practice articulating your thought process and problem-solving approaches.

Typically, interviews at TD Bank vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Write a query to get the top 3 highest employee salaries by department. Given the employees and departments table, write a query to get the top 3 highest employee salaries by department. If the department contains less than 3 employees, the top 2 or the top 1 highest salaries should be listed. The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order.

• Write a function to combine sorted integer lists while maintaining sorted order. Given a list of sorted integer lists, write a function sort_lists to create a combined list while maintaining sorted order without importing any libraries or using the 'sort' or 'sorted' functions in Python.

• Write a function to swap nodes in a singly linked list. Given the head of a singly linked list represented as a ListNode, and two zero-indexed positions x and y, write a function swap_node which swaps the positions of nodes x and y and returns the new head. Note that you cannot simply swap the values of these nodes; you must swap these two using pointer manipulation.

• How would you build a strategy to find the best businesses to reach out to? You are a credit card company looking to partner with more merchants. You have 100K small businesses to choose from but can only reach out to 1,000. How would you develop a strategy to identify the best businesses to contact?

• How would you investigate a decrease in credit card payment amounts per transaction? You work for a financial company and notice a decrease in the credit card payment amount per transaction. How would you investigate the cause of this change?

• What features would you include in a model to predict a no-show for pizza orders? Imagine you run a pizza franchise and face a problem with many no-shows after customers place their orders. What features would you include in a model to predict a no-show?

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates the forest. Additionally, why would we use random forest over other algorithms such as logistic regression?

• How would you build a fraud detection model with a text messaging service for a bank? You work at a bank that wants to build a model to detect fraud on the platform. The bank also wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction, allowing the customer to approve or deny the transaction with a text response. How would you build this model?

• What is the relationship between PCA and K-means clustering? Explain the relationship between Principal Component Analysis (PCA) and K-means clustering.

What is the average salary for a Data Scientist at Td bank?

Q: What does the interview process for a Data Scientist position at TD Bank look like? The interview process at TD Bank typically involves multiple rounds. The first round is usually a behavioral interview, followed by a technical interview that includes coding and data analysis questions, and finally, a case presentation. You may be asked to review credit default models, answer SQL, Python, and SAS questions, and present your reasoning for selecting specific machine learning models.

Q: What are the main job responsibilities of a Data Scientist at TD Bank? As a Data Scientist at TD Bank, you will be responsible for developing predictive and prescriptive analytics, building complex statistical and machine learning models, and delivering data-driven insights for strategic decision-making. You'll work closely with business owners, design and deliver enterprise analytic solutions, and lead significant projects with enterprise-wide impact.

Q: What skills are required to be successful in the Data Scientist role at TD Bank? To excel in the Data Scientist role at TD Bank, you should have a strong background in SQL, Python, SAS, and machine learning techniques. Proficiency in data mining, statistical modeling, and data visualization is essential. Advanced experience with big data tools like Hadoop is highly valued, along with excellent analytical, problem-solving, and communication skills.

Q: What is the company culture like at TD Bank? TD Bank prides itself on having a positive work environment that values service to the business, quality, innovation, and teamwork. They are committed to fostering a diverse and inclusive workplace where employees feel valued and supported. Career development and mentorship opportunities are plentiful, helping you reach your professional goals.

Q: How can I best prepare for an interview at TD Bank? Preparing for an interview at TD Bank involves researching the company, understanding its culture, and practicing common interview questions. Utilize resources like Interview Query to practice your technical skills and review data science concepts. Be ready to discuss your past projects, your approach to problem-solving, and how your skills align with the job requirements.

Conclusion

Exploring a Data Scientist position at TD Bank offers a unique and rewarding career path characterized by thorough and engaging interview processes. With multiple rounds that include behavioral, technical, and case presentation interviews, candidates are given ample opportunity to showcase their skills and knowledge. The experience is marked by interactions with professional team members and insightful discussions, particularly in areas such as SQL, Python, and machine learning models for credit default predictions.

From coding challenges and data analysis exercises to in-depth behavioral interviews, the selection process is designed to identify well-rounded individuals who excel not only technically but also culturally fit into TD Bank's collaborative environment. Candidates can expect to delve deeply into their resumes, discuss feature importance in decision trees, and describe machine learning concepts in a comprehensible manner.

If you want more insights into the company, check out our main TD Bank Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about TD Bank's interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every TD Bank interview question and challenge.

Good luck with your interview!