Preparing for Your Interview at Argus Information & Advisory Services

Interviewing at Argus Information & Advisory Services can be an exciting yet nerve-wracking experience. This leading financial services company is known for its rigorous hiring process aimed at identifying top analytical talent. In this article, we’ll explore some of the most common Argus interview questions along with tips to help you prepare effectively.

Overview of Argus Information & Advisory Services

Argus Information & Advisory Services is a renowned provider of data-driven solutions to financial institutions across the globe With a vast repository of proprietary data, Argus leverages analytics and modeling to equip clients with strategic insights and operational enhancements. The company is recognized for its expertise in risk management, asset valuation, credit risk analysis, and portfolio surveillance

Argus caters to prominent names in banking, insurance, asset management, and more. Some of their major clients include JP Morgan, BlackRock, and Goldman Sachs. Their unique ability to turn data into value-added insights has fueled Argus’ growth into a trusted leader in financial analytics and advisory services.

Argus Interview Process

The interview process at Argus typically comprises:

• Initial phone/video screening with HR
• Technical + behavioral interviews (on-site/virtual)
• Case studies
• Reference checks
• Offer

The complexity and number of interview rounds depend on the role. Expect questions testing your technical proficiency, problem-solving skills, and ability to communicate insights effectively. Case studies are commonly used to assess analytical thinking.

For technical roles, SQL and Python proficiency is tested. The overall process can last 2-3 weeks. Demonstrating analytical rigor, intellectual curiosity, and communication skills is key to success.

Common Argus Interview Questions

Let’s look at some frequent Argus interview questions and how to ace them:

Technical Questions

1. How would you check for outliers in a large dataset using SQL?

   • Outliers can skew analysis so identifying them is crucial. I would use SQL techniques like boxplots and scatterplots first to visually spot outliers. For large data, querying to find values outside a specific percentile range works better than visual inspection. Analytic functions like PERCENTILE_CONT() are useful here. The INTERQUARTILE_RANGE() function can also help set outlier thresholds.

2. Explain your experience with statistical modeling or machine learning techniques.

   • In a past role, I developed a logistic regression model using Python to predict loan default risk. After cleaning and preparing the historical data, I evaluated multiple ML algorithms before finalizing logistic regression due to its interpretability and ease of implementation, which were important for stakeholder buy-in. I used techniques like L1 regularization to reduce overfitting and validated the model thoroughly before deployment. The model improved default risk prediction by 18%.

3. How would you perform time series analysis and forecasting using Python?

   • I have good experience building ARIMA and SARIMA models for time series forecasting in Python. I would start by checking the data for stationarity, seasonality, trends etc. using visual plots and Dickey-Fuller tests. Then I would determine the AR and MA components by analyzing the ACF and PACF plots. SARIMA expands on this by incorporating seasonal dynamics too. I would optimize model parameters using grid search and cross-validation. Finally, I would evaluate forecast accuracy on a test set before deploying the model.

Behavioral Questions

1. Tell me about a time you influenced a key business decision using your analysis.

   • In my last role, I performed sentiment analysis on customer feedback data to gain insights into pain points in our new product. My analysis clearly showed customers were struggling with integration issues. I summarized the key findings in an impactful presentation for senior executives. As a result, they decided to fast-track improvements to the product integration framework, leading to a noticeable increase in customer satisfaction.

2. How would you convince stakeholders to act on your recommendations despite initial resistance?

   • First, I would seek to understand their concerns and hesitations about my recommendations. Then, I would emphasize how my solution specifically addresses our business goals and use data, numbers, case studies etc. to build a compelling fact-based case. If needed, I would modify my initial recommendations based on their feedback to address their reservations. Follow-ups are also key – I would share periodic progress updates to maintain buy-in. The aim is to find common ground and help stakeholders visualize how my solution benefits them.

3. Tell me about a time you had to rapidly learn a new technical skill. How did you approach it?

   • When I joined my previous role, I had limited SQL knowledge but it was critical to learn it quickly. I immediately enrolled in an online SQL course and supplemented that with practical learning using demo datasets. I reached out to coworkers proficient in SQL to understand how they leveraged it in their work. Within a few weeks, I was querying datasets and incorporating SQL efficiently into my projects. The key was immersing myself fully into learning – I treated it like a part-time job after work hours. This experience demonstrated my ability to adapt rapidly when required.

Case Interview Questions

1. How would you design a credit risk scoring model for a peer-to-peer lending platform?

   • I would approach this in a structured manner:

   • Understand the lending business model and risk factors to identify relevant variables

   • Collect historical loan data and clean it to extract useful features

   • Explore correlations of different attributes with default rate

   • Try out models like logistic regression, decision trees, random forests etc. and compare their predictive accuracy

   • Fine-tune the best model using regularization, hyperparameter optimization etc.

   • Rigorously test model performance on unseen data using K-fold validation

   • Analyze model outputs to isolate key drivers of default risk

   • Build an application for loan officers to generate automated risk scores before approving loans

2. Our client is observing significantly higher portfolio risk than their competitors. How would you help identify the root cause?

   • My first step would be to analyze the portfolio composition – are they overexposed to certain risky assets or sectors compared to competitors? I would also assess if they are using different risk models or assumptions relative to the industry. Detailed loan-level analysis could reveal specific cohorts driving higher risk. I would evaluate their risk forecasting models and benchmarks to check for limitations. For identified issues, I would suggest refinements to their models or portfolio balances to align risk appetite with industry standards. Ongoing monitoring through stress testing and scenario analysis could help prevent recurrences.

Questions for You

• Can you elaborate on the day-to-day responsibilities in this role? I want to ensure my skills align well.

• How does the analytics team collaborate with other business units at Argus? I’m interested to know more about cross-team interactions.

• What are some of the most complex or challenging analytical problems I could get the opportunity to work on?

Tips for Preparing Effectively

• Research the company and role thoroughly to target your preparation
• Practice case interviews using online resources and consider peer mock interviews
• Brush up on technical skills like SQL, Python, statistical modeling etc.
• Review your resume and be ready to elaborate on all aspects
• Prepare stories highlighting your analytical skills, business acumen, problem-solving and communication abilities
• Project confidence and intellectual curiosity throughout the interviews

With diligent preparation using these tips and common questions as a guide, you can greatly improve your chances of interview success at Argus. The key is showcasing your technical expertise, business insight, and communication skills, along with the ability to drive impact through analytics.