How to Get Entry-Level Machine Learning Jobs

I cracked jobs at Amazon, Microsoft, and TikTok, using this roadmap. Now it's your turn to use it. Every week, I speak to job seekers who say things like: “I’ve applied to 200+ jobs but barely got a response.” And I get it. Mass applying feels productive, but it usually leads to mass rejection When I was job hunting, I knew I had to play the game differently. Here’s what actually helped me land offers from top companies: 1. Find the right role I didn’t waste time scrolling endlessly on job boards. Instead, I went straight to the official careers pages. For Meta: metacareers dot com I set job alerts and searched for specific roles like: Software Engineer - Machine Learning - New York Not just “Software Engineer.” Bonus tip: I kept an eye on hiring managers’ posts — they often hint at open roles before they’re listed. 2. Apply the right way I applied within 24–48 hours of a role going live. Early birds really do have an edge. I tailored my resume to include the right keywords from the job description (ATS optimization is non-negotiable). And I didn’t hit “Apply” unless I was also working on finding a referral. 3. Make recruiters notice me Before I reached out, I fixed my LinkedIn: ✅ Clear headline ✅ Strong featured section ✅ Keywords that matched my target roles I turned on “Open to Work” (visible only to recruiters) And started engaging with recruiters’ posts before sending a DM. 4. Network like a PRO I searched for people who had recently joined these companies. Commented on their posts. Then sent personalized DMs like: “Hey [Name], I came across your work at Meta — really insightful! I’m exploring roles in [XYZ] and would love to learn more about your experience. Open to a quick chat?” No cold asks, but real conversations. 5. Prepare like I already have an interview I tracked questions in Notion. Did mock interviews on Interviewing(dot)io and Pramp. And I worked with a coach to tighten up my stories and delivery. Within a few days of following this strategy, I landed multiple interviews with the top companies. Save this post if you’re job hunting right now. P.S. Follow me if you are an Indian job seeker in the U.S. I talk about job search, interview prep, and more.

A lot of people trying to break into data science spend months, sometime even years... Learning the wrong things. They dive deep into neural networks, reinforcement learning, and complex machine learning algorithms, thinking that’s what will land them a job. But when they finally start applying, they realize the job market is looking for something else. So... what do companies want then? Most companies hiring data scientists aren’t looking for cutting-edge AI research. They need professionals who can: + Work with messy, real-world data – Cleaning, structuring, and analyzing data is 80% of the job. If you can’t handle raw datasets, machine learning skills won’t matter. + Use SQL fluently – If you can’t query a database efficiently, you’ll struggle in almost any data role. SQL is still one of the most in-demand skills in the field. + Apply basic statistical thinking – Companies don’t need fancy deep learning models for most problems. They need people who understand probability, regression, and how to make sense of data. + Communicate insights effectively – Data scientists who can translate numbers into clear, actionable recommendations will always be more valuable than those who just build models. + Understand the business problem first – Companies care about ROI, not algorithm complexity. If you don’t connect your work to business impact, you’ll be seen as just another technical hire. So... what mistakes are people doing? - Overloading on Theory Without Application – Learning every ML algorithm but never actually working on real datasets. - Ignoring SQL and Data Wrangling – Machine learning is useless if you can’t efficiently extract and clean data. - Building Portfolio Projects With No Business Impact – Instead of copying Kaggle projects, focus on solving problems that could help a company save money, improve efficiency, or make better decisions. How would I approach it? 1. Master SQL and data manipulation before diving into machine learning. 2. Prioritize problem-solving with real business datasets, not just pre-cleaned Kaggle data. 3. Learn to present insights clearly and tell a compelling data story. Focus on building projects that demonstrate impact, not just model accuracy. The data science job market isn’t looking for people who know the latest AI trends—it’s looking for people who can solve real problems with data. If you’re trying to break into the field, ask yourself: Are you learning what actually matters, or just what looks impressive on paper? Would love to hear your thoughts.

Here’s exactly what I’d do if I wanted to get a data science job without prior experience: When I started, my only background was in teaching. I knew I needed to prove I could handle real data science work, so I built my own portfolio of projects and made sure they were the first thing on my resume. These projects became my “experience.” Start simple: ⮑ Find an interesting dataset and do a thorough EDA ⮑ Build a dashboard to explain your findings clearly ⮑ Try projects with machine learning, deep learning, and NLP. Since AI is trending, explore generative AI and RAG. My go-to starting points: ⮑ Coursera guided projects ⮑ Kaggle datasets and notebooks. Show your work publicly: ⮑ Feature it on LinkedIn and write about it ⮑ Post GitHub repos with clear READMEs and visualizations. ✨Your portfolio is the proof hiring managers want to see. P.S. How are you building yours? #DataSistah 🔔Follow me for more tips on how to accelerate your data science career. ♻️Reshare to help others.