You have significant industry skills, your passion has made you put in lots of hours,and energy, into your machine learning and data science projects. Now that you have geared yourself up, you apply for available jobs matching your relevant skills and profile.Day’s pass and Bingo, you get a mail from a recruiter regarding a scheduled interviewwith your dream company!

Now that you have been shortlisted, you start revising your Machine learning
concepts, you refer various data science course, get used to the jargons, revising the maths behind algorithms, and many more. But there’s another important thing that should be on your preparation list which has the maximum potential to grab that dream opportunity which is demonstrating and walking through your best Machine learning project to the technical panel! Interviews can be intimidating but explaining the project you put blood and sweat in, shouldn’t be!

Well, in this Blog, we have listed some very basic and simple steps which you can keep in mind while structuring your answer and briefing about your project. These steps have been ordered in a quite precise way and referring to each of these steps can definitely help you get your dream job you have been wishing for.

1. Selecting a right project resonating with the company you apply.


This is the most crucial and important in the process. It is not a great practice to talk about projects which are irrelevant to the company you’ve applied to. For example, if you have applied for an e-commerce company, I would go with a retail dataset, for a fintech company I would choose a loan application dataset, and for a healthcare company I would prefer to pick a dataset surrounding medical backgrounds. The trick is to pick a project based on your target audience. We would always recommend to have a brief look about the domain of interest of the company you have been selected for. You can always rely on Kaggle and UCI to get a spectrum of datasets suited for the domain of the project resonating with the company’s domain.

Afterall the problem or project you pick says a lot about your maturity, technical skills and your creativity, and in the process, you are demonstrating your taste and business acumen. Also dealing and selecting the right project with an end-to-end approach will surely serve a cherry on cake.

2. Giving an intuition about the data source.


Besides the goal of selecting projects with the most benefit to the organization, there comes a need to explain the intuition about the data source as well. Maybe you did the project to learn data science and extracted the required data via Kaggle/UCI, it’s important to mention the data source. You can even mention that it was some open-source data available on the internet freely. Even if the data source was mined using third party APIs (say twitter data). Whatever be the case, make sure you are revealing the source of your data.


Why? This builds confidence and transparency between you and the recruiter, which in turn improves the chances that the recruiter shows increasing interest in your project! Additionally, make sure you explain the details about the single row feature. One single row represents exactly what the problem is trying to solve. A single row comprises all the features used and the dependent target variable that the Machine Learning model will predict. It comprises intuition regarding features, dependent and independent variables, etc.


3. Exploratory data analysis in BRIEF.


Firstly, the BRIEF stands for “Be Rational in Explaining Features”, this can be a crucial step because your dataset will have a lot of features and explaining every feature in depth will indirectly steal a lot of time and may indicate to the interviewer that you cannot prioritize well.

Instead, we have a solution for this, it's always better to skim through exploratory data analysis and share or explain the insights and intuitions about the data explaining charts and plots you worked on regarding the data. This shows your analytical approach which can be the remarkable trait for any data analyst or data scientist.


Afterall, it’s your approach to solve the problem, in which the interviewer is interested in. Also, the period of your intuition should be changed accordingly if you feel there is a need to stress on a particular feature to be able to explain the model building.

4. Explaining the Algorithm selected for training.

There comes a common question about “Explaining why a particular algorithm was selected?” For any given machine learning problem, numerous algorithms can be applied and multiple models can be generated. Though we have a number of performance metrics to evaluate a model, it’s not wise to implement every algorithm for every problem.


For any algorithm selected, there are various factors which define the effectiveness of that particular algorithm, in which the interviewer is interested in. These are the proved factors which serve the following purpose,

● Interpretability
● The number of data points and features
● Data format
● Training time
● Prediction time
● Memory requirements

However, Performance may seem like the most obvious metric when selecting an
algorithm for a machine learning task. This shows the interviewer your technical knowledge and decision making to select, why a particular algorithm was selected over other algorithms available.

5. Deploying your Model.

Learning a way or two to deploy your model makes sure that you know how to take your project in the production phase and make it easier for a layman to use it without having to see the technicalities that go behind it.

It is always highly beneficial if you have your project model deployed as an API or a Web Application on any of the platforms, one of the easiest platforms you can deploy your Machine learning model is Heroku or Streamlit. Apart from impressing the recruiter, you can use it to show the world your brand-new application!

6. Preparing some back-up questions.

How is data collected at your company? Who will I be working with? How will the projects I work on align to business goals?

Questions like the one above are bound to be asked by you once you finish explaining your amazing project to the interviewer. And most of the time, it is a good sign! This shows the interviewer that you are genuinely interested to know more about your work.

We end these points with a bonus tip, I.e., “Breath in and Breathe out!”. Don’t rush to finish! Enjoy the process of the interview and leave a beautiful impression about you. So, breathe…..

Well, these were the steps from our side to convey your project to your interviewer
smoothly. Make sure to leave no tiny detail out of your answer. Afterall, it’s not how many hours you put in but how concisely you can convey all the Technical as well as the Business aspects of it in the short period of time. We from Board Infinity, wish you all the best for your next interview, and we hope you bring the Trophy home! (i.e., Grab your dream job!).