Скачать или смотреть How to Quantify Impact as a Data Scientist, Data Engineer, or ML Engineer? | Ep2

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In this episode of the Data Neighbor Podcast, Hai and Sravya dive deep into one of the most critical yet often misunderstood topics across data science, machine learning, data engineering, business intelligence, AI: IMPACT. Whether you're seeking a promotion, crafting your resume, or preparing for interviews, understanding and articulating the impact of your work is essential.

We cover:

What impact really means and how to recognize it in your work.
Common misconceptions about impact and how to avoid "non-impactful" efforts.
A framework to define impact in three ways: moving metrics, influencing roadmaps, and driving process improvements.
Practical tips for tracking and showcasing your contributions effectively.
Real-world examples of how data professionals across various roles—analysts, data scientists, ML engineers—can demonstrate their impact.

This episode is packed with actionable advice for anyone looking to stand out in their data career. Learn how to track your work, follow up with stakeholders, and showcase your unique contributions in a way that highlights your value.

Episode chapters:

00:00 Introduction to Impact
02:21 What Is NOT Impact?
05:20 Defining Impact: The Three Types
09:02 Why Impact Matters for Your Career
14:26 Overcoming Challenges in Recognizing Impact
17:07 Mindset Shift: Owning and Sharing Your Impact
26:28 Practical Tips for Tracking and Documenting Impact
28:08 Role-Specific Examples of Impact
39:48 Recap: The Three Questions for Defining Impact
41:18 Closing Thoughts and Call to Action

Connect with Hai, Sravya, and Shane
Hai Guan : https://linkedin.openinapp.co/4qi1r
Sravya Madipalli : https://linkedin.openinapp.co/9be8c
Shane Butler : https://linkedin.openinapp.co/b02fe

If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below

Spotify: https://open.spotify.com/show/3BZnavS...
Apple https://podcasts.apple.com/us/podcast...



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