The Data Science field has exceeded all estimates of career opportunities made in the last five years and outperformed with more than 2 times of the projected numbers. The Data Science career unarguably stood as the most desired job.
Also it is the fact that there are millions of Data Science jobs left unfilled every year due to the lack of qualified Data Science professionals. In the year 2021, there were close to 3.2 million of Data Science jobs left unfilled worldwide, of which around 2.0 million jobs are from Asia Pacific region.
On the other hand, many aspirants who pursued a Data Science course, gained strong foundation knowledge, did many learning projects, are finding it difficult to get a job in the Data Science field.
So the question: Why are many Data Science aspirants with good preparation finding it difficult to get their first job in the field of Data Science?
And the answer: Structured interview preparation. In this article we shall discuss “How to prepare for Data Science Job Interviews” in a structured manner.
4-Phase Data Science Job Interview Preparation
DataMites®, a leading institute for Data Science training, have trained more than 25,000+ Data Science aspiring professionals and enabled thousands of them to transition to Data Science careers.
With this deep experience over 6 years into Data Science training, DataMites® formulated a 4-Phase approach for preparing Data Science aspirants to get job ready.
1. Technical Readiness
2. Resume Preparation
3. Interview Skills
4. Job Application Strategy
1. Technical Readiness
A strong knowledge of Data Science concepts and practical experience in applying these concepts is one of the most important aspects of getting a Data Science job ready. Data Science is a vast subject and mastering all the Data Science concepts is difficult.
Technical readiness indicates that the candidate should be able to demonstrate key concepts which are relevant to the practical application of Data Science concepts to real-world applications.
The topics of the Data Science that is considered as essentials are as below
1. Programming - Python for Data Science
2. Statistics and Mathematics
3. Data Preparation
4. Feature Engineering
5. Machine Learning
6. Data Science Model Deployment
7. Projects & Internship
A strong foundation in key concepts of above topics and applied knowledge in solving data problems and machine learning modelling suffice the essential technical readiness for Data Science and Machine Learning Job interviews.
Resume Preparation
The resume is the first touch point with your potential employer. As per the research, most of the interviewers/initial resume scanners make-up their minds in less than a minute on glancing through the resume. So it is very important to present your resume not only in an impactful manner but also in a clean, simple and professional manner.
Here are the few areas to strengthen your resume:
First is First - Get through ATS
In most cases, Applicant Tracking System (ATS) has a built-in system to filter relevant resumes. These ATS filter systems work with simple keyword matching and density mechanisms. This means that ATS gives a high score for the resume which mentions the same keywords as in the job description(KD) key skills requirement section.
First is first, the resume has to get past the ATS, so it is important to fine tune the resume inline with the job description for every job application.
Prioritise Relevant and Important
It is a common practice to write the experience in the resume in chronological order. But reviewers would be more interested in the skills and experience that are relevant to the job description, so that they can evaluate the resume for the job.
It is recommended that the relevant skills and experience for the job be placed at the time and highlighted. The rest of the information can be presented below in chronological order.
Remove Not Relevant Information
It is equally important to either remove or minimise the not relevant information. It would be a distraction to mention your achievements, experiences and skills that are not relevant to the current job you are applying for..
Highlight Technical Skills
Always use a separate section- “Technical Skill”, to highlight the technical skills that are relevant to the job as mentioned in the job description.
Mention Participation in Relevant Events
It is a good practice to mentioned the participation in relevant events such as webinars, tech meets, conferences, etc., Only mention the events that are relevant to the job
Interview Skills
The data science interviews are usually on relevant skills and practical experiences, it is also important to have good Interview skills including sitting posture, eye contact, concise answers, professional mannerism, display positive attitude, being a good listener etc. There are a plethora of articles available to improve your interview skills but the key is to practise.
Mock interviews with constructive feedback are valuable to improve interview skills.
Job Application Strategy
Finally, you should have a job application strategy. It is common that you need to attend multiple job interviews before starting to get successes. Before you venture into applying for jobs, you should have a clear understanding of your career ambitions, your skills and other considerations. Here are the few steps to start with your job application strategy.
1. Identify the right role
2. Fine Tune Resume for each Job
3. Channels: Job Portals, Company Pages, etc.,
4. Follow up on Applications
5. Network for References
DataMites® has a dedicated job placement team who have worked with more than 10,000 Data Science career aspirants for the past 5 years. DataMites provides free counselling on job strategy and helps you with resume services. Contact DataMites through 1800-313-3434
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