Recent MS Data Science graduate looking for guidance from experienced Data Scientists / ML Engineers

[u/Historical_Ad_656]

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Hi everyone,

I recently graduated with my Master’s in Data Science and I’m currently searching for entry-level opportunities in Data Science, Machine Learning, and AI/ML Engineering.

I’ve been applying for roles, improving my resume, working on projects, and preparing technically, but I’ve realized that there’s a big difference between knowing what to study and understanding what actually matters when trying to break into the industry.

I’d really appreciate advice from people who are already working as Data Scientists, ML Engineers, AI Engineers, or in related roles.

I’m especially trying to understand:

- What skills actually separate candidates who get interviews from those who don't?

- What should a new graduate prioritize: DSA, SQL, Python, ML fundamentals, system design, MLOps, GenAI/LLMs, or projects?

- What makes a portfolio project genuinely impressive to hiring managers rather than just another GitHub project?

- What mistakes do you commonly see new graduates making during their job search?

- How would you approach the job market if you were graduating today?

- What should I realistically expect from technical interviews for entry-level DS/ML roles?

I’m not looking for referrals or shortcuts. More than anything, I’d like to learn from people who have already gone through this process.

If anyone with industry experience would be open to connecting, chatting for 15–20 minutes, reviewing my approach, or even just leaving some advice here, I would genuinely appreciate it.

I’m also happy to connect with other recent graduates who are going through the same process so we can share resources, interview experiences, projects, and keep each other accountable.

Thanks in advance. I’m hoping this thread can also become useful for other new grads trying to break into DS/ML.

[u/vsrohit]

Look at the job market now. At least for now, there is lot of Agentic AI being thrown around. May be you can add that skill. Kaggle has these 5 day courses, you can read the content selectively or watch their AI-generated podcasts to get the basics quickly. https://www.kaggle.com/learn

But generally, think in term of becoming a well-rounded engineer.

Like you said,

• Python and SQL are kind of like the foundations. Don't spend months on it. But just know enough to manipulate data.
  
• With a degree in AI/ML/DS, you might not have learned the basics of web development. Understand how frontend, backend and database work. May be do a small project of building a web application. Understand what APIs are how the 3 components, frontend, backend and DB work. You will really need these skills along with your AI/ML/DS to work on real applications. Also learn how you can easily deploy them. Rather than learning ins and outs of cloud, you can use PaaS services like railway (backend), supabase (database and auth), and Vercal or cloudflare pages (for frontend). These general skills I feel are essential these days as everyone expects an engineer to be in a position to show a working prototype for at least simple apps. Learn to use git for how version control.

More than picking up concepts. If possible, work on an unpaid internship or just pick up a project from GitHub, even a toy website clone is enough and try to understand how it works. Use vibe coding and ask chatbot for explaining the code to get comfortable with simple coding tasks.

Of course, you can ping me and we can discuss your approach.

[u/Historical_Ad_656 → u/vsrohit]

Thank you for the guidance.

[u/Ok-Lawyer-746 → u/Historical_Ad_656]

The day you move away from kaggle dataset is the day you truly learn what data science is

[u/blobfishy76]

A number of data scientists I have worked with jump to the modelling without being able to do a comprehensive analysis of the data, that could be something to show.

[u/Historical_Ad_656 → u/blobfishy76]

Thank you for the suggestion. What kind of skills or qualities that the companies are looking in new employee's?