UBS Quantitative Analyst Interview Questions, Process, and Tips

Q1. Past project experience
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I have worked on various quantitative analysis projects in finance, risk management, and data science.
• Developed predictive models using machine learning algorithms

• Conducted statistical analysis to identify trends and patterns in data

• Implemented quantitative strategies for portfolio optimization

• Utilized programming languages such as Python, R, and SQL

• Collaborated with cross-functional teams to deliver actionable insight
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Q1. Fixed income fundamentals
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Q2. Previous job functions
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Q1. Previous job functions
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Q2. Role in decision making
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Quantitative analysts play a crucial role in decision making by providing data-driven insights and recommendations.
• Utilize statistical models to analyze data and identify trends

• Develop quantitative strategies to optimize decision making processes

• Collaborate with stakeholders to understand business objectives and provide relevant analysis

• Present findings and recommendations to support informed decision making
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Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Q1. Detailed project questions
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Q2. Explain completed gen ai project
Ans. 
Developed a generative AI model to create realistic images of fictional characters.
• Used GANs (Generative Adversarial Networks) to generate new images based on existing data.

• Trained the model on a dataset of character images from various sources.

• Implemented techniques like style transfer to enhance the diversity and creativity of generated images.

• Evaluated the model's performance based on image quality metrics and user
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Q1. How do you define model Gini?
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Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
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XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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Question on Probability and basic aptitude questions

Q1. Question were related to Past projects
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Q2. SQL and Excel medium level questions
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Q1. 1) What is attention mechanism (since it was mentionned in my resume) 2) What is the sde of the Heston model? 3) What is implied volatility? 4) What is PCA (machine learning question)? 5) How matrices are...read more
Ans. 
Answers to various quantitative research questions
• Attention mechanism is a key component in deep learning models that allows the model to focus on specific parts of the input sequence.

• SDE stands for Stochastic Differential Equation in the context of the Heston model used in quantitative finance.

• Implied volatility is the market's expectation of future volatility implied by the prices of options.

• PCA (Principal Component ...read more
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