How I Got a 75 LPA Data Scientist Job in Singapore

Key wellness, self-care, and productivity strategies (as discussed)

• Don’t just “learn”; practice intensely

  • Prefer hands-on/practical coding over passive theory videos.
  • For every 1 hour of lecture, do 3–4 hours of practice.
  • Avoid “I watched it so I know it” thinking—build and work through problems.

• Use structured daily continuity (even when sick)

  • If you can’t study long, still study in smaller chunks:
    • 2–4 hours if needed
    • or 10 hours when possible
  • Don’t miss days entirely; “skipping” harms progress more than reducing time.
  • When overwhelmed by real-life events, keep the routine “minimum effective effort.”

• Leverage motivation during anxiety/depression

  • Treat your “own circumstances” (family pressure, breakup, illness, uncertainty) as fuel to keep going.
  • Aim for mental stability despite career anxiety and emotional stress.

• Make career choices that protect your health and future

  • The speaker describes periods of poor health/obesity during upskilling.
  • Despite health issues, they continued studying with flexibility—staying active in progress, not all-or-nothing.

• Build resilience through “conflict for a future”

  • They fought family pressure to pursue tech learning.
  • Takeaway: if you don’t push back, you may never get your future opportunity.

• Networking as a productivity system (LinkedIn-first)

  • Create a strategy to connect with the right people:
    • send connection requests to data scientists/data analysts
    • avoid irrelevant contacts (e.g., general students/college crowds)
  • Emphasize being an extrovert for career-building (communicate, talk, reach out).
  • Use English for communication and storytelling (data science is framed as storytelling).

• Job application strategy focused on outreach + volume

  • Apply broadly (not only the “ideal” role/region), including:
    • internships, freshers, paid internships, data analyst roles (then leveraging data science skills)
    • applied to 300+ jobs
  • Include targeted follow-up messages:
    • ask the hiring contact to shortlist your resume or tell you what’s wrong so you can revise.
  • Be “shameless” in outreach—reduce overthinking and keep sending messages.

• Resume customization for match-fit

  • Create multiple resume templates (the speaker used 5):
    • vary projects and positioning depending on the company needs.
  • Tailor which projects you highlight to match each job’s expectations.

• Use internships as a career bridge

  • Since direct job openings can be hard, they recommend:
    • paid internships (especially abroad/Southeast Asia) to gain experience quickly
    • internships can convert into permanent roles
  • Timing matters:
    • apply for internships with seasonal cycles (speaker mentioned windows in summer and December).

• AI/learning tools approach: use AI for hints, not answers

  • Use AI tools (e.g., ChatGPT) to:
    • get guidance and hints
    • but avoid copy-pasting direct solutions
  • The goal is to force understanding and independent problem-solving.

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Key productivity “methodologies” summarized

• Practice-first learning loop

  • Watch short/targeted learning content
  • Immediately do practical work for hours
  • Reinforce with documentation + problem-solving

• Minimum-study continuity plan

  • If busy/sick/grieving:
    • keep a study streak with reduced hours
    • never fully stop for long

• Networking pipeline

  • LinkedIn as primary source
  • Connect with relevant professionals
  • Communicate to ask for shortlist feedback

• Application engine

  • Apply to many roles/types
  • Follow up directly
  • Stop overthinking when there’s no response
  • Iterate (resume edits via feedback when possible)

• Resume templating

  • Build multiple templates
  • Match projects to each job description

• Internship-first path

  • Choose paid internships to remove experience barriers
  • Aim for conversion to full-time employment

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Presenters / sources

• Presenter: Abhishek (data scientist; the speaker of the video)
• Sources/tools mentioned: LinkedIn, ChatGPT (also referred to as “Chair GPT”), Stack Overflow, Python, NumPy, Pandas, Matplotlib/Seaborn (mentioned as “Seaborn”), Scikit-learn, SQL, “Machine Learning,” “Big Data” (frameworks not specified), plus Google/ChatGPT/GPD references as search/assistant tools.