Careers in AI rarely follow a straight line. Over the years, I have moved across multiple roles across academia and industry - starting as a Software Development Engineer, then transitioning to a Data Scientist, a Research Engineer, an ML Scientist, and eventually an Applied Scientist. Each shift came with uncertainty, self-doubt, and the fear of “starting over”. But each role also shaped how I think, build, and collaborate in ways I couldn’t have anticipated at the start.

In this talk, I will share my journey across these roles and what each taught me - from writing production-ready systems, to reasoning with data, to building ML models at scale. I will discuss how Python has been a constant thread through these transitions, acting as both a technical foundation and a career enabler. More importantly, this talk is for anyone who feels stuck, unsure, or intimidated by the idea of changing roles - whether you’re a developer curious about ML, a data scientist thinking about engineering, or simply someone navigating an evolving tech landscape - I will share practical lessons, mindset shifts, and concrete advice to help you view role changes not as setbacks, but as powerful opportunities for growth.

Learning Objectives:

By the end of this talk, attendees will be able to:

• Understand how different AI roles (SDE, Data Scientist, ML Scientist, Applied Scientist) differ and overlap
• Recognize how skills gained in one role can compound and add value in another
• Identify common fears and misconceptions around changing roles in tech
• Learn practical strategies for navigating role transitions without “starting from scratch”
• Gain confidence to explore career paths that align with their evolving interests and strengths

Talk Outline (30 minutes):

1. Introduction: A Non-Linear Career in AI (5 min)
   • Why career paths in AI are rarely linear
   • My journey across multiple roles and why I made each transition
2. What Each Role Taught Me (10 min)
   • Software Development Engineer: building reliable, scalable systems
   • Data Scientist: asking the right questions and working with ambiguity
   • ML Scientist: modeling, experimentation, and evaluation
   • Applied Scientist: bridging research, engineering, and real-world impact
3. The Role of Python as a Career Backbone (5 min)
   • How Python enabled mobility across roles
   • Libraries, ecosystems, and transferable skills
4. Fear, Identity, and Career Transitions (7 min)
   • Common fears: “I am not qualified”, “I will lose seniority”, “It’s too late”
     • How to reframe role changes as skill expansion, not regression
     • What I would do differently if I were starting today
5. Closing Thoughts & Q&A (3 min)
   • Actionable advice for attendees considering a change
   • Resources and next steps

Target Audience:

• Python developers curious about AI/ML
• Data scientists or ML practitioners considering role changes
• Early- and mid-career engineers navigating career uncertainty
• Anyone feeling “boxed in” by their current job title