What Recruiters Won't Tell You About Entry-Level Data Analyst Jobs

Introduction

In the next five years, the demand for entry level data analysts jobs is expected to grow by over 25%. Businesses are now considering making more informed decisions based on statistics and data. However, the market has a gap where most data analysts struggle to find good opportunities. Most data analysts and freshers complete their studies, have the required skills and end up not getting the right position according to their capabilities.

This is because companies need much more than just technical skills in their candidates. This is followed by certain hidden practices that the recruiters of different companies follow. Through this blog, let us understand the hidden practices that are not discussed by recruiters and crack the code of landing the right data analyst entry level jobs.

Everything You Need To Know: "Entry-Level" Paradox Carried Out By Recruiters

As an active job seeker have you come across job postings with 'entry-level' written on them. But often have a minimum of 2+ years experience required mentioned within the details. This is a standard practice in the recruiting industry often used as a filtering tactic. Most companies and their recruiters use this terminology to attract a large number of job seekers.

While only those candidates get selected who have prior experience. Hiring a fresher and training them is more expensive for a company than directly hiring someone experienced. For example when hiring for a data analyst job in New York, a candidate who contributes from the

very first day is valuable. The candidates are expected to have experience through internships, freelance work and independent projects.

So what could be the solution? The best solution to tackle this problem is by presenting a portfolio containing projects completed during your study. This can include analyzed datasets on Kaggle, automated reports on SQL, etc. The hack here is to present your practical skills and stand out even for an entry-level position.

Mandatory Skills to Get Hired But Are Never Mentioned In the Description

Mastering basic skills such as Python, SQL and Excel are mandatory for a candidate applying to entry level data analyst jobs. But these skills alone cannot promise you a job. So what will? Let's find out!

1.    Most employers in different departments do not need just numbers of the data that you have found. Develop the skills to present numbers, analyses, and patterns and provide the essential trends in data. The real deal is analyzing data and presenting it to employers in the simplest form for informed decision-making.

2.    Regardless of the sector you apply for, finance, banking, healthcare, etc. it is essential to understand the impact that your analytical skills can contribute to the revenue of the company. This is an invaluable skill, which can make you irreplaceable for any company.

3.    A data analyst's job in USA in any company is much more than just finding patterns in data. It is about spotting trends, identifying cracks in the system and bringing forth efficient solutions.

The Resume Mistakes to Avoid

Resumes have to stand out to make an impact. If you plan on sliding through with a basic resume then it won't go that far. Especially in the data analytics field, the resumes go through an ATS. Application tracking system helps recruiters in scanning through a large number of resumes within minutes. This helps them save time and get an accurate match according to the search intent. Here is how the ATS works:

1.    Mention quantifiable contributions that you made during an internship as an entry level data analyst. Numbers tend to catch the attention of the software. In this respect mention the percentage of difference you brought to the employers.

2.    Recruiters as well as employers are looking forward to seeing proof of your experience. Linked your portfolio, link to your OPTnation, Kaggle competition results, etc.

3.    The ATS system uses tailored keywords to categorize resumes. Use the term or application-based words in your resume for example 'data visualization', 'Python scripting', etc.

Hidden Job Market and Alternate Hiring Sources

More than 70% of hiring happens through referrals, internal hiring, and networking. Hence job postings do not exist in this case, which is why applying to data analyst jobs only through postings is not enough. Here's what you should do!

Networking

Recruiting is a time consuming process which is why recruiters often prefer hiring through employee referrals. More than 85% of positions are filled through networking even before a job posting goes live on the company's website. The ideal thing to do here would be connecting with the right people on LinkedIn, keeping in touch with recruiters and posting on industry news actively.

Where to be active?

Recruiters often hire from alternate platforms such as OPTnation, Kaggle and Stack Overflow. This is why posting your projects on these platforms and updating them on a regular basis can work efficiently to your advantage. Industry events for data analysts in USA are also one such source of directly being involved with the hiring team. Most industry professionals attend industry meet-ups and seminars which is a great way of meeting leaders in your industry and impressing them with your personality and knowledge.

Interview Hacks That Recruiters Don’t Want You To Know!

Data analyst is one such job role where candidates assume that the only skill they need to get through is knowledge and experience. This is not true! Recruiters often have a complicated filtering method to choose the right candidate.

The interview process

Technical skills and knowledge are just the top layer of what recruiters look for during the hiring process of entry level data analysts. You will be given real-world challenges from the industry to understand, analyse and provide a practical solution. Recruiters are often looking for candidates that think out of the box and provide innovative and fresh solutions. This not only gives you an upper edge over other candidates but also increases your chances of getting selected.

Conclusion

Getting hired for an entry-level data analyst job has its own tricks and hacks. The more you dive deeper into the hiring process the easier it gets to be chosen by reputed companies. Recruiters hire only those candidates that offer more than just their technical skills, they expect problem-solving capabilities and the ability to think outside the box.

These skills in a data analyst are crucial in the time of crisis for any company. The simple solution to all these limitations is creating a resume that is too strong to neglect. This can be achieved by first making it search-friendly according to the software used by recruiters. Secondly, mention previous real-world projects and problem-solving cases that you performed.

More than 70 % of hiring is done through referrals and networks. This is why networking with the right people in the industry is another important factor to consider while looking for data entry jobs in New York. Understanding these hidden tricks and hacks not only helps candidates get ahead of others but also increases their chances of getting hired as an entry level data analyst.