The questions below represent the style and rigor of the Akur8 technical evaluation. They are drawn from actual candidate experiences and are intended to help you identify patterns in the company's questioning style. Do not simply memorize answers; ensure you understand the foundational logic and mathematics behind every concept.

Machine Learning Mathematics

This category tests your ability to go beyond the surface and write out the exact mathematical mechanics of standard algorithms.

• Explain the mathematical model behind Principal Component Analysis (PCA) and write out the exact formula.
• Derive the cost function for logistic regression.
• How do you mathematically define the Gini impurity in a decision tree?
• Write out the formula for an L2 regularization penalty and explain how it affects the loss function gradients.
• Explain the mathematical difference between a Generative and a Discriminative model.

Actuarial Science & Applied Data Science

These questions evaluate your ability to adapt data science techniques to the strict, highly regulated domain of insurance rate-making.

• Walk me through a data science use case specifically applied to actuarial science.
• How would you handle a highly imbalanced dataset when predicting insurance fraud?
• Explain the concept of pure premium and how you would model it using machine learning.
• Why is interpretability crucial in insurance pricing, and how do you achieve it mathematically?
• How do you evaluate the performance of a model predicting rare, high-severity claims?

Algorithms, Statistics, and Pseudo-Code

Often presented as a timed test, this category assesses your raw problem-solving speed and statistical knowledge.

• Write an algorithm in pseudo-code to sort a dataset based on a custom statistical metric.
• Describe the specific parameters of a Random Forest model and how tuning them impacts variance and bias.
• Write pseudo-code to implement a basic gradient descent optimization from scratch.
• What is the statistical formula for covariance, and how does it relate to correlation?
• Given a specific scenario, calculate the conditional probability using Bayes' theorem.

Actuarial Science and Business Use Cases

Since Akur8 builds software for actuaries, your ability to understand and solve actuarial problems is heavily scrutinized. You do not necessarily need to be a certified actuary, but you must demonstrate a strong grasp of how data science applies to insurance pricing, risk assessment, and claims modeling. Strong candidates seamlessly blend predictive modeling with the strict regulatory and interpretability requirements of the insurance industry.

Be ready to go over:

• Insurance Pricing Models – Understanding frequency and severity modeling, pure premium calculation, and generalized linear models (GLMs).
• Interpretability (Transparent AI) – Why black-box models are unacceptable in rate-making and how to enforce transparency in predictive algorithms.
• Risk Evaluation – Dealing with highly imbalanced datasets, heavy-tailed distributions, and rare events (e.g., large claims).
• Advanced concepts (less common) – Credibility theory, survival analysis applied to churn, and spatial modeling for geographic risk.

Example questions or scenarios:

• "Walk me through a data science use case specifically applied to actuarial pricing."
• "How would you model claim severity given that the data is heavily right-skewed?"
• "Why might an actuary prefer a Generalized Linear Model over a Random Forest for rate-making, and how can we bridge that gap?"

Statistics and Algorithmic Logic

Your foundational computer science and statistical skills are evaluated through rapid problem-solving exercises. You will be expected to write clean, efficient pseudo-code and answer fundamental statistical questions, often under a strict time limit. Interviewers look for precision, speed, and the ability to translate a statistical requirement into a logical algorithmic flow.

Be ready to go over:

• Probability and Statistics – Bayes' theorem, expected value, variance, probability distributions, and hypothesis testing.
• Algorithm Design – Writing algorithms in pseudo-code to solve data manipulation or statistical computation problems.
• Time and Space Complexity – Evaluating the efficiency of the pseudo-code you write.
• Advanced concepts (less common) – Dynamic programming, advanced Monte Carlo simulations, and stochastic processes.

Example questions or scenarios:

• "Write an algorithm in pseudo-code to efficiently compute the moving average of a streaming dataset."
• "Given a specific probability distribution, write out the formula for its variance and expected value."
• "Design a pseudo-code function to sample from a custom distribution without using external statistical libraries."

Key Responsibilities

As a Data Scientist at Akur8, your day-to-day work revolves around researching, designing, and implementing the core machine learning algorithms that power the company's pricing platform. You will spend a significant amount of time diving deep into mathematical literature, prototyping new models, and writing complex logic to ensure these models are both highly accurate and entirely interpretable. Your deliverables are not just insights, but production-ready algorithmic frameworks that integrate directly into the Akur8 product.

Collaboration is a massive part of this role. You will work side-by-side with actuaries, translating their domain-specific needs and regulatory constraints into mathematical optimization problems. You will also partner closely with the engineering team to ensure that your pseudo-code and mathematical models can be scaled efficiently within the platform's architecture.

You will frequently drive initiatives focused on "Transparent AI." This means taking traditionally opaque models and engineering novel ways to extract linear, understandable rules from them. Whether you are optimizing a proprietary algorithm, benchmarking model performance against traditional actuarial methods, or participating in deep technical exchanges with your peers, your work will consistently push the boundaries of applied mathematics in the insurtech sector.

Role Requirements & Qualifications

To be competitive for the Data Scientist position at Akur8, you must possess a rigorous academic background and a proven track record of mathematical excellence combined with strong programming skills.

• Must-have skills – Exceptional grasp of linear algebra, calculus, and mathematical statistics. Proficiency in Python or R for data manipulation and modeling. Deep knowledge of the mathematical formulas underlying core machine learning algorithms (e.g., PCA, GLMs, decision trees). Strong ability to write logical, efficient pseudo-code.
• Experience level – Typically requires a Master’s degree or Ph.D. in Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative field. Candidates usually have 2+ years of highly technical data science experience, though exceptional entry-level candidates with rigorous academic research backgrounds are often considered.
• Soft skills – Unwavering composure under technical pressure. The ability to articulate complex mathematical concepts clearly to both technical and domain-specific audiences (like actuaries). Receptiveness to direct feedback and a rigorous, detail-oriented mindset.
• Nice-to-have skills – Prior experience in the insurance industry or specific knowledge of actuarial pricing models. Experience with system design and scaling machine learning algorithms in a cloud environment. Familiarity with C++ or other high-performance compiled languages.

Frequently Asked Questions

Q: Do I really need to memorize mathematical formulas for the interviews? Yes. Candidate experiences indicate that interviewers at Akur8 expect you to know the exact mathematical models and formulas behind standard algorithms like PCA. Be prepared to write them out and explain them, rather than just providing the logical intuition.

Q: Is a background in actuarial science strictly required? While not always strictly required, it is highly beneficial. You will face use-case interviews centered specifically on actuarial data science. If you do not have an actuarial background, you must independently research insurance pricing, GLMs, and rate-making principles before your interview.

Q: What is the format of the algorithmic questions? You will likely face a mix of timed written tests and live discussions. The algorithmic questions often require you to write pseudo-code rather than compiling actual Python or C++ code. The focus is on your logical structuring and algorithmic efficiency.

Q: How long does the interview process typically take? The process is generally efficient and transparent. Once past the initial recruiter screen, the technical rounds can be scheduled quickly, often wrapping up within two to three weeks depending on your availability and the team's schedule.

Q: What is the culture like during the technical interviews? The technical interviews are known to be highly rigorous and sometimes blunt. Interviewers will push you hard on your mathematical knowledge and may challenge your answers directly. Maintain your composure, defend your logic, and be ready to admit if you need to derive a formula step-by-step.

Other General Tips

• Master the Math Behind the Magic: Do not rely on your knowledge of scikit-learn APIs. Spend your preparation time reviewing linear algebra, calculus, and the mathematical derivations of foundational ML models. If you claim to know an algorithm, you must know its math.
• Practice Pseudo-Code Under Time Pressure: You may face a strict 20-minute timed test for algorithmic and statistical questions. Practice writing out logic quickly on a whiteboard or blank text editor without relying on an IDE's autocomplete features.