How to Impress a Recruiter with ML Projects

How to Impress a Recruiter with ML Projects

Blog | Board Infinity
Blog | Board Infinity

You have significant industry skills, and your passion has made you put in lots of hours, and energy, into your machine learning and data science projects. Once you apply for available jobs matching your relevant skills and profile and get a scheduled interview with your dream company, how exactly do you prepare to impress the recruiter?

You start revising your Machine learning concepts, refer to various data science courses, get used to the jargon, revise the maths behind algorithms, and much more. But another important thing that should be on your preparation list which has the maximum potential to grab that dream opportunity is walking through and demonstrating your best Machine Learning project to the technical panel! Interviews can be intimidating but explaining the project you put blood and sweat into, 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 the dream job you have been wishing for.

1. Selecting the Right Project resonating with the Company you apply to

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, go with a retail dataset, for a fintech company, choose a loan application dataset, and for a healthcare company, prefer to pick a dataset surrounding medical backgrounds. The trick is to pick a project based on your target audience.

It is recommended to have a brief look at 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.

After all, 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 with and selecting the right project with an end-to-end approach will surely serve as a cherry on the 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.

Exploratory data analysis

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

After all, it’s your approach to solve the problem, in which the interviewer is interested. 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 that define the effectiveness of that particular algorithm, in which the interviewer is interested. These are the proved factors that 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 into 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 Backup Questions

How is data collected at your company? Who will I be working with? How will the projects I work on align with 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 on you. So, breathe…..

Conclusion

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. After all, 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 a 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!).

If you want to learn more about machine learning, you can check out our Machine Learning Course to learn the scientific study of algorithms and statistical models that computer systems use. Master algorithms like using regression, clustering & classification. Start from scratch and learn from industry experts about how a Machine Learning Engineer impacts the world.

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