7 Principles That Separate Great Data Scientists From The Rest

From Someone Who Learned These The Hard Way

Hello Friends,

Happy Friday! After 7 years at Amazon and countless hard-learned lessons, I finally cracked the code of what truly separates great data scientists from the rest. Today, I'm sharing the principles I wish someone had taught me before I made all those mistakes on my journey to becoming a senior data scientist.

But first, a quick note from today's sponsor - Monte Carlo Data: if you want to discover how other data teams accelerate growth, create new business value, and drive Data + AI trust at scale, register for IMPACT 2024 — a 1-day virtual summit that brings together the world’s top data leaders and practitioners. (Register here)

Alright, let’s get to the 7 principles!

1. Technical Excellence Is Just The “Entry Fee”

❌ Myth: Senior DS = More Complex Models

✅ Reality: The real complexity lies in solving ambiguous business problems and building systems that scale. Many senior projects focus on improving data pipelines or ML iteration systems - not researching cutting-edge algorithms.

2. Your Job Isn't Building Models - It's Being a Trusted Advisor

❌ Myth: Stakeholders know exactly what they want

✅ Reality: It's our job to ask the right questions, show different solutions, and guide them in the right direction. The most impactful work often doesn't even look like 'data science.'

3. Speed Without Strategy Is a Career Killer

❌ Myth: Urgent requests need immediate action

✅ Reality: Lost trust is harder to fix than any bug. If they've waited 3 hours, they can wait 30 more minutes for accuracy. Counter-intuitively, the more 'urgent' the request, the MORE questions you should ask.

4. Simple Solutions > Complex Models

❌ Myth: Advanced algorithms = Better solutions

✅ Reality: Being highly technical doesn't give you the privilege to look down on simple solutions. If an Excel IF-THEN statement solves the problem, that's perfect. Stakeholders don't care about LLMs vs. rule-based algorithms - they care about results.

5. Translate Technical Jargon Into Business Impact In Plain English.

When talking to non-tech stakeholders:

❌ Don't say: "The model has 85% accuracy in predicting churn."

✅ Say: "We could save $1.2M annually by preventing 850 customers from leaving."

6. Help Your Stakeholders Become Heroes to Their Bosses

❌ Myth: Great analysis sells itself

✅ Reality: Package your work so well that your stakeholders can't wait to present it to their bosses. Create presentation-ready slides, craft compelling narratives, and tie everything to their KPIs. Your stakeholders should be able to walk into any meeting and confidently champion your work without needing to translate a single insight.

7. Success Is Measured in Adoption, Not Accuracy

❌ Myth: High model accuracy = Project success

✅ Reality: If stakeholders don't trust you or understand your solution, even a 99% accurate model will fail. It’s also important to make it easy for the stakeholders to adopt your solutions with their tech stack. The best model isn't the most accurate one—it's the one that gets implemented.

🎯 Ready to put these principles into action?

I'm opening my complete Senior Data Scientist Playbook, where I share the exact frameworks and communication strategies that helped me and other data scientists transform from 'technical experts' to 'trusted business advisors'.

You'll learn how to:

• Master stakeholder management and effective communication

• Lead high-impact data science & ML projects with confidence

• Position yourself for senior-level promotions

⏰ Only 2 spots left. Apply here: https://maven.com/dalianaliu/ds-career

What do you think about these principles? Reply with your thoughts or any questions about the course.

See you next time,

Daliana