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Some data professionals make $80k while others crack $130k? Why? The secret isn't just in the title - it's in understanding the crucial differences between these roles.I've spent hours researching and interviewing professionals to create the ultimate guide comparing Data Analysts, Scientists, Engineers, and ML Engineers. Here's a sneak peek: • Data Analysts aren't just Excel wizards • Data Scientists do more than build models • Data Engineers are the unsung heroes • ML Engineers? They're not just Scientists who code But here's the thing - your career path choice shouldn't just be about the salary. I break down everything you need to know in my latest YouTube video: - Exact technical skills required - Real salary ranges - Day-to-day responsibilities - Which role matches YOUR personality (I built a secret app for you!) Don't let confusion hold your career back. Watch the full video to find your perfect data role (link in comments).

How we provided 10 qualified candidates within a week for our client: Obviously needed qualified data analysts and data scientists to help improve its data platform. The candidates needed to fit the desired salary range and have the following technical skill sets: 01/ Must have: SQL 02/ Nice to have: Python, Ruby, and a statistics background. They faced a challenge: Data scientists are in high demand due to the rise of data-driven decision-making. Finding these experts is challenging and time-consuming. That’s why Obviously turned to Bridge. Our cooperation with Obviously included the following stages: Stage 1: Creating a job requisition Stage 2: Using our sourcing engine to find candidates in Latin America Stage 3: A/B testing candidate outreach campaigns Stage 4: Communicating with candidates Results? We provided 5-10 technically qualified candidates per week using our proprietary sourcing engine and technically savvy human recruiters. The agency’s Senior Data Product Manager only had to interview these candidates and make hires. We are moving forward. Thank you, team 💙 Case study -https://lnkd.in/g9wbH3Ht

Without experience, you won't get a job in data. (Bad news for new grads and pivoting professionals) So how do you cover the experience gap? Projects. 1. Take a course 2. Grab a dataset 3. List a few objectives 4. Create your project 5. Add it to your portfolio This is how you "create" experience. Guided projects help you learn skills. Personal projects help you master them. ___ BONUS tip - if you're seeking an entry-level data role, add a "Projects" section to your resume. Talk about a 2-3 projects the same as you would for work experience.

"I’m not technical enough for a data job." That's a common myth and it's holding you back. I used to think this way too. But, here's the truth: Data careers aren't only for math geniuses and coding experts. There are diverse roles at every level. You don't need to be a math wiz or understand every programming language. Just start. Here's the reality: ❌ Data science is less about sophisticated math models and more about understanding the business problem. ❌ A data analyst's job is to analyze data, not code. ❌ You don't need to know every technology in existence—just the ones you'll use. Stop denying yourself the chance to enter data careers. All you need is a willingness to learn, a solid work ethic, and a desire to make significant changes. The most important thing you need is the willingness to solve the business problem from the data and save them from getting into any situation. Go ahead, finesse your resume, build a portfolio, learn the required technologies, master the techniques, update your LinkedIn, and start applying. You got this.

Listen to Matt Mike on how to cover up experience gap.

This is so great, initiative and effort are so powerful.

Which industries does Tiered serve? Our tools are not limited to specific roles and industries. We understand careers are different, and what might work for an accountant might not work for a data scientist. That's why we personalize our insights to every individual. Get in touch with us to get started: https://tiered.app/ #Careers #CareerGuide

After August, now I got an interview for data analyst position at Nimble I was asked to do a simple test on Google sheets. The output was GBP 74000 whereas my output came out to be GBP 76000. They rejected me on the spot stating we are looking for someone with more experience. 5 minutes later, I rechecked and formula was working fine. Then where was the mistake ? I mistakenly dragged down an extra GBP 2000 making it a calculation of 13 month instead of 12 months. I agree it was a mistake but am I so wrong that I get directly rejected ?. You state that you've found a better candidate, then why do I still see the vacancy still open on multiple platforms. Not just this company, I've seen in multiple ones. "We are looking for someone with more hands on experience". no worries, you go hire robots. Why waste money on hiring. Now I realise why you write in job description: attention to detail, work under pressure, multitasking and excellent interpersonal skills BUT sorry your expected salary is out of our budget or second scam, "salary not disclosed" This is the mentality why I don't apply to jobs anymore