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Our typical assessment process
1. CV review
Once you send us your CV and examples of your work, our technical recruiting team will assess your fit for the role.
2. Technical challenge
For some roles you will be required to undertake an online technical challenge. This enables us to get a look into how you code and what methods and languages you use. You will likely also have a call with one of our technical recruiters, who will ask about your experience, and perhaps pose a few competency based questions so you get the chance to show us some examples of your work and skills.
3. Initial face-to-face
In these meetings, we will take a deeper dive into the knowledge and expertise you could bring to the team. This often takes the form of a case study or whiteboarding session. This will normally last 1-2 hours and take place via video conference or in person with 1-2 team members from the team you would be joining.
4. Final face-to-face
For the final stage you’ll typically have a 1-2 hour in-person technical interview with a team lead or manager, plus an interview with a member of another team who will assess your core non-technical skills. You will also get the chance to ask all the questions you like about QB and the role.
Frequently Asked Questions
We hire people with all sorts of backgrounds. We do value extra-curricular learning courses such as these if it means you are learning new and relevant skillsets in the ML space. Part of the learning and development programmes offered at QB include training courses such as these.
Every project we undertake is different and our approaches therefore vary accordingly. The nature of our work is incredibly diverse which is why people love working here. We discuss what Tech stack and infrastructure we will implement with our clients. Our focus will always be on solving the particular challenge using the most efficient and appropriate methods - whether they be cutting edge or more classic ML / software engineering tools and methods. We wouldn't necessarily use a Deep Learning Neural Network if we feel we can solve the problem using a Random Forest for instance. We explore different methodologies and assess the most beneficial based on the project.
Our Data Scientists and Data Engineers are all client-facing. This means they spend time on client site typically at the start and at the end of projects - and sporadically in-between at times. We encourage everyone to work from our QB offices as much as possible to incubate learning and best practices. QB Machine Learning Engineers and Designers also work on client site from time to time, though travel is typically less essential for them.
Members of QB Labs (our product and delivery-focused teams that are not directly client-facing that develop exciting new products and toolkits) require minimal travel.
Upon joining QB, you will work with our Professional Development team to discuss how best you can achieve your career goals. We take these goals into account when staffing people on projects - for instance, someone with a keen desire to focus on a particular sector like pharmaceuticals will likely be given the opportunity to do this. This is all subject to live projects of course, but staffing is a two-way conversation.
People joining QB at entry through to mid-level client facing positions typically work on one project at a time. More senior colleagues will then usually work across a few projects providing guidance and advice in this manner.
This varies; we have been working with some clients for over a year through different phases of wholesale transformational projects for instance. A typical project is two to three months long.
Our Data Scientists, Engineers and MLEs are the data experts and they work with the QB Project Manager to test different ideas and models at the start of a project. Based on these efforts, they will then propose a model and work with the client to develop and implement it.
There are overlaps in skillsets between the two roles. Broadly speaking, our Data Scientists focus on ML modelling, whereas our MLEs have software engineering backgrounds and focus on productionizing these ML models with best-in-class software engineering and coding practices. There is an increasing convergence of the two skillsets and both teams work very closely.
Our Data Engineers work closely with our Data Scientists to curate, transform and construct features which feed directly into our modelling approaches. Data Engineers model the client's data landscape and define the Tech stack to be provisioned by our Infrastructure team. Our Data Scientists work closely with them and focus mostly on the ML modelling part of the project.
Please check out Dr Sanja Franic's blog post
We now offer summer internships in the Data Science and Data Engineering teams in certain offices. We typically look for people with some commercial or academic experience after graduation, though not all our roles require this - please check the job description for more details. For Data Scientist roles, we prefer candidates to have either a MSc or PhD.
We typically open applications around March or April time (subject to change - check the job opening if in doubt).
As an intern you will work on live engagements and projects and thus are an extension of our teams. Our internships are an integral part of our people strategy as they leverage the apprenticeship model that QB is built upon. Our interns receive on-the-job training and apprenticeship on the skills required in their discipline, working with senior mentors in the team. You might be working on a live client project, or on an exciting new product for example. You work with project managers and deep technical experts from different disciplines on how to approach problem solving in a QuantumBlack context and how to build relationships with clients and colleagues. All our internships are paid.
Yes. One of the benefits of being part of McKinsey is access to first class immigration lawyers. Visa processes are always subject to local immigration laws but broadly speaking we can sponsor candidates if they pass the labour market tests.
No. We are data experts first and foremost. Our bar to entry is very high for technical and problem-solving skillsets (see assessment process), however most roles do not require an MBA or a business background – check the job description for more details. We hire many people straight from academia.
Yes! This is subject to openings, but broadly speaking, yes we will offer full-time roles to people who demonstrate QB qualities and drive.
Please check with your Recruiter.
Yes. We have offices around the world and due to the nature of our international work and the global McKinsey operating model, there are opportunities to do this. We encourage new joiners to discuss such plans with your Professional Development Manager as early as possible.
Being at the forefront of our field is demanding and we recognise the effort that QB colleagues put in to everything they do. We promote a healthy work-life balance and our focus is on the highest quality of output, not the longest work weeks. Our guild leads are responsible for setting a healthy expectation of working hours and we do not believe QB people need to work the long hours traditionally associated with consulting. Depending on the office you will join, you could have yoga sessions, free gym classes or meditation rooms. We are always open to ways of improving this part of QB life! As part of McKinsey, all QB employees have access to McKinsey benefits and policies. One of these is what we call Take Time where you can take up to one month of unpaid leave per annum. We offer private healthcare insurance to all our employees, as well as other benefits. Check out our benefits here.