Qube Research & Technologies Interview Guide

QRT interview process

QRT uses an application, a technical coding assessment and interviews for its internship and graduate research and technology pathways. Interviews can take place in person or over Microsoft Teams and examine technical reasoning, unfamiliar problems and collaboration.

The preparation becomes more specialised once you select a team. Research involves predictive signals and experimental judgement; trading involves live strategies and risk; software engineering and quantitative development involve building the systems that let both disciplines operate.

1. Apply for the relevant discipline

   Choose research, trading, software engineering or quantitative development, then explain the fit using technical projects and genuine interests. The questions in the application itself are part of the evaluation.

2. Complete a programming assessment

   Student research and technology openings include a coding challenge measuring programming fundamentals and problem solving. Expect to turn a description into a correct algorithm and handle edge cases under time pressure.

3. Work through technical interviews

   Research discussions focus on mathematical reasoning, models, data and projects. Technology interviews probe code, design choices and practical engineering, while trading discussions connect quantitative decisions to execution and risk.

Application and role selection

QRT recruits students in quantitative subjects for research and trading, and students in computing and related technical subjects for engineering. Current research internship openings include students pursuing bachelor’s, master’s and doctoral degrees; technology eligibility is defined separately for each location and discipline.

Start with the QRT careers page and match your background to an actual opening. Describe one substantial piece of work with a measurable result, the obstacle you resolved and what you would improve with more time.

Make the motivation answers technical

The research and trading application asks what attracts you to QRT, which modelling techniques interest you and why you want to work in systematic investing. In the Asia research application, candidates also explain their preferred asset classes or strategies in 80 to 100 words.

Internships, graduates and experienced researchers

For internships, present projects and learning potential. For graduate opportunities, demonstrate deeper ownership of an implementation or study. For experienced positions, prepare evidence of an entire research or engineering cycle, including the production constraints and results you can discuss without breaching confidentiality.

QRT online assessment and coding challenge

The coding assessment is a core part of the student research and technology selection process. It tests whether you can write a correct solution, explain the algorithm and make sensible choices about complexity and implementation.

Use a timed environment such as HackerRank for preparation. For research, work in Python with lists, dictionaries, sorting and numerical data; for technology, use the language relevant to the role and practise translating edge cases into tests.

EverythingQuant practice benchmark: solve two moderately difficult algorithm questions in 60 minutes, then reserve ten more minutes to test empty inputs, boundaries and repeated values. This is a training target, not a QRT assessment specification.

At review, explain your invariant in one sentence and give the time and space complexity. The EverythingQuant programming questions and Data Structures and Algorithms in Python course are useful for targeted practice.

Technical interviews by role

Prepare for the specific problem your prospective team solves. QRT research, trading and technology roles share a mathematical culture, but they require different evidence of skill.

How to discuss a systematic research project

A strong research project discussion follows the signal from observation to deployment. Use a clear sequence so the interviewer can challenge individual assumptions without losing the overall reasoning.

Practise with a QRT data challenge

QRT has contributed a Challenge Data exercise on forecasting asset allocation performance. It asks participants to classify whether candidate allocations will perform well over the next session, offering a concrete way to explore financial prediction with time series data.

Build a transparent baseline first, then compare it with a regularised model or a tree based approach using an appropriate validation split. Record the evaluation metric, features available at prediction time and one failure case you can explain in an interview.

A subtle timing error to catch

Suppose a dataset attaches a company metric to Monday but the file becomes available Tuesday afternoon. A strategy trading Tuesday morning cannot use the Monday value; that would place future information into the decision.

In a practice interview, state the timestamp convention first, redraw the split and rerun the baseline using only data available by decision time. Then describe how you would test data revisions, stale rows and missing observations.

Technical conversations and later interviews

Shortlisted student applicants meet interviewers on site or through Microsoft Teams. Prepare for an interactive discussion: describe your approach, make an assumption explicit, adapt when challenged and verify the conclusion.

For a research discussion, choose one project with a credible negative result or revised hypothesis. Walk through data cleaning, the evaluation metric, the strongest baseline and what changed after error analysis.

For engineering, choose a real design decision and explain the requirements, bottleneck and tests. Be ready to trade a faster implementation against readability, correctness and recovery after failure.

Four original QRT practice questions with solutions

These EverythingQuant exercises are designed around the research, coding and trading skills above. They are original preparation problems, not questions taken from a QRT interview.

sum(x * x) = 10
sum(x * (y - 1)) = 10

OLS slope   = 10 / 10       = 1.0000
Ridge slope = 10 / (10 + 2) = 0.8333
Lasso slope = (10 - 2) / 10 = 0.8000
Intercept   = 1

E_V = 1 + 0.6 * E_V + 0.4 * E_C
E_C = 1 + 0.3 * E_V + 0.7 * 0

E_V = 2.5 + E_C
E_C = 1 + 0.3 * (2.5 + E_C)
0.7 * E_C = 1.75
E_C = 2.5
E_V = 5.0

from collections import deque

def breakout_flags(prices, window):
    if window <= 0:
        raise ValueError("window must be positive")

    maxima = deque()  # (index, price)
    flags = []

    for i, price in enumerate(prices):
        # Keep only indices from the preceding window.
        while maxima and maxima[0][0] < i - window:
            maxima.popleft()

        is_breakout = (
            i >= window
            and bool(maxima)
            and price > maxima[0][1]
        )
        flags.append(is_breakout)

        # A newer equal or higher price dominates an older one.
        while maxima and maxima[-1][1] <= price:
            maxima.pop()
        maxima.append((i, price))

    return flags

assert breakout_flags([10, 12, 11, 15, 13], 2) == [
    False, False, False, True, False
]
assert breakout_flags([5, 5, 5], 2) == [False] * 3
assert breakout_flags([], 3) == []

Trade every signal:
0.40 * (15 - 5) + 0.60 * (3 - 5)
= 4.0 - 1.2 = 2.8 basis points per opportunity

Trade strong signals only:
0.40 * (15 - 5) + 0.60 * 0
= 4.0 basis points per opportunity

For additional probability practice, work through Probability for Quants or the probability interview questions before attempting the problems with no solution visible.

How difficult are QRT interviews?

The difficult part is often moving between clean textbook reasoning and the constraints of real research or production software. A correct equation is useful, but interviewers also need to see that you know when its assumptions fail.

For research, the pressure points are statistical interpretation, clear model selection and defending a project. For technology, they are writing robust code, diagnosing edge cases and justifying performance choices. For trading, they are separating a forecast from a profitable and controlled action.

EverythingQuant readiness benchmarks

• Algorithms: finish two medium coding problems in one hour, and explain an invariant and complexity for each.
• Research: derive an OLS estimator, explain Ridge and Lasso, solve a small Markov problem and identify leakage in a timed case.
• Trading: evaluate a trade after spread, fees and slippage, then identify the risk that invalidates the initial decision.
• Projects: give a coherent five minute walkthrough and answer three probing follow ups without contradicting the original assumptions.

These are practical self checks, not firm pass marks. Use them to find the weakest skill before spending more time on broad revision.

Four week QRT preparation plan

Use this EverythingQuant plan as a structured starting point, then allocate more time to your weakest discipline. Each week ends with a deliverable you can inspect rather than a vague reading target.

For research applicants, spend the final sessions defending out of sample validation and data availability. For quant developers and software engineers, spend them testing the coding solution and explaining system behaviour under faults or load.

Frequently asked questions

Sources and preparation resources

Use QRT’s live career openings to choose your track and verify eligibility. The additional interview resources help identify useful technical question families while the practice exercises above provide independent, fully worked preparation.