Techstar Group is a dynamic organization specializing in providing innovative technology solutions to its clients. We are currently looking to hire a Data Scientist with a focus on Anti-Money Laundering (AML) for a remote, U.S.-based position, preferably in the Eastern Time Zone. The role offers a unique opportunity to work with cutting-edge AI technologies, such as Microsoft Fabric, CoPilots, and Azure OpenAI/GPT, alongside a global team of senior architects.

If you're a seasoned professional with experience in software design, data modeling, and statistical analysis, this could be your next exciting challenge. Our ideal candidate has a background in government procurement, AML investigations, and knowledge of the Microsoft Azure ecosystem. In this guide from Interview Query, we’ll navigate you through the interview process, offer insights, and share valuable tips to help you succeed.

Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Techstar Group as a Data Scientist. 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. Don't forget to highlight relevant skills and mention your work experiences, particularly those related to software design, AI, and ML technologies.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Techstar Group 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.

In some cases, the hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

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 Techstar Group Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around AI technologies, machine learning solutions (e.g., classification, regression, clustering), and programming in languages such as Python, T-SQL, or R.

In addition to this, your proficiency in hypothesis testing, probability distributions, and the Microsoft toolset in AI and ML (e.g., Azure Machine Learning, Azure Cognitive Services) may also be assessed during the round. Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the Techstar Group office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Scientist role at Techstar Group.

Quick Tips For Techstar Group Data Scientist Interviews

• Know Your Technologies: Techstar Group places great emphasis on proficiency with Microsoft AI and ML tools, such as Azure Machine Learning and Azure Cognitive Services. Familiarize yourself deeply with these tools and how you can leverage them in various scenarios.
• Be Data-Driven: Ensure you can demonstrate your ability to derive business-driving insights from data. Brush up on your statistical analysis skills and be ready to discuss your approaches to complex data problems.
• Understand the Domain: Given the focus on financial management, fraud detection, and anti-money laundering, having a good grasp of domain-specific knowledge will be highly advantageous.

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

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• 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.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should have a height difference of at most one between the left and right subtrees of all nodes.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you figure out where the mouse is using the fewest number of scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Devise a strategy to locate the mouse using the fewest scans.

• How would you decide which Dashers do deliveries in NYC and Charlotte for Doordash? Doordash is launching delivery services in New York City and Charlotte. Develop a process for selecting dashers (delivery drivers) and determine if the criteria for selection should be the same for both cities.

• What factors could have biased Jetco's study on boarding times? Jetco, a new airline, was found to have the fastest average boarding times in a study. Identify potential biases in this result and what factors you would investigate.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Design a two-week A/B test to evaluate a pricing increase and determine if it is a good business decision.

• How much should we budget for the coupon initiative in total? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider. The app services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one of them will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

• What is a confidence interval for a statistic? Explain what a confidence interval is, why it is useful, and how to calculate it.

• What is the probability that item X would be found on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary compared to less complicated regression models.

• How would you justify the complexity of building a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which random forest generates its forest. Additionally, discuss why one might choose random forest over other algorithms like logistic regression.

• How would you explain linear regression to a child, a first-year college student, and a seasoned mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Ensure your explanations are tailored to each audience's understanding level.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

What is the average salary for a Data Scientist at Techstar Group?

Q: What does the Data Scientist - AML position at Techstar Group entail?
The Data Scientist - AML role involves working with a worldwide team on cutting-edge AI technologies like Microsoft Fabric, CoPilots, and Azure OpenAI/GPT. Responsibilities include data preparation, modeling and statistical analysis, coding with AI & ML, and working on financial crime detection and anti-money laundering projects.

Q: What qualifications are required for the Data Scientist position?
You should have 3-5 years of software design experience, familiarity with Agile Development Processes, GitHub CoPilot, and Azure DevOps. Experience with Microsoft Azure Cloud Services and prior work with government procurement or investigations is highly favorable.

Q: What specific technical skills are necessary for this role?
Proficiency in machine learning solutions (classification, regression, clustering, etc.), scripting languages like T-SQL, Python, and R, and understanding of Microsoft tools such as Azure Machine Learning and Azure Cognitive Services. Experience in big-data software engineering concepts (Apache Spark, CI/CD, Docker, etc.) is also crucial.

Q: What is the duration and location for this position?
The position is remote, preferably aligned with the US Eastern Time Zone, and has a duration of 5+ months with a potential extension.

Q: How can I prepare for an interview with Techstar Group for this role?
To prepare for the interview, research Techstar Group and the specific projects you'll be working on. Review your knowledge in AI & ML technologies, big-data software engineering, and Microsoft Azure services. Practice common interview questions using resources available on Interview Query.

If you want more insights about the company, check out our main Techstar Group 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 Techstar Group’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 Techstar Group machine learning engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!