Keep learning: Your career is never truly done

If there’s one thing that wasn’t that clear to me when I was younger, it’s that your career is never truly done (while you’re still alive and well). For example, as a PhD student, I felt like I’d be “done” once I graduated. But that wasn’t the case, and it still isn’t the case 12 years later. I just find new things to do.

In the spirit of never done, I recently joined Eli Gündüz of Careersy Coaching on his podcast to talk about my career journey. While I’m more comfortable with writing than with unscripted public speaking, it was good to practise the latter, and I didn’t waffle too much. You can listen to the chat on Spotify or below:

In the spirit of the times, here’s an AI-generated summary of the top ten takeaways from the chat, with some minor edits for clarity:

1. Career Journey: Yanir, originally from Israel, moved to Australia and transitioned from software engineering to data science, eventually becoming an independent consultant focused on helping startups ship data-intensive solutions.
2. Data Science Evolution: Yanir noted how the field of data science has evolved, especially since 2012, when it gained popularity. He also discussed the changing terminologies and how roles like AI and machine learning engineering have become more mainstream.
3. Educational Background: Yanir noted how his PhD helped him move into data science, as an unintended result of wanting to work on interesting things.
4. Internship Experience: They discussed how Yanir secured an internship at Google Sydney during his PhD, which helped his career by providing industry experience alongside academic research.
5. Consulting Approach: Yanir shared his approach to consulting, focusing on providing advisory services and helping startups navigate the complexities of data and AI, especially in the climate tech and energy transition sectors.
6. Tools vs. Concepts: Yanir stressed the importance of focusing on underlying concepts rather than specific tools, as tools in the data science industry constantly change, while core concepts are evergreen.
7. Challenges in Data Science Recruitment: The discussion highlighted the difficulties in recruiting and defining roles in data science due to the broad and overlapping responsibilities of various titles like data scientist, data engineer, and machine learning engineer.
8. AI in Recruitment: The conversation touched on the impact of AI in recruitment, discussing how AI can be used to streamline candidate selection but also the potential biases and limitations of relying solely on AI-driven processes.
9. Independent Consulting: Yanir reflected on their decision to leave a stable job to pursue independent consulting, driven by a desire to focus on climate-related projects and have more control over his work.
10. Personal Branding and LinkedIn: Yanir touched on the importance of personal branding, particularly on LinkedIn, where he’s been actively posting and refining his profile to attract clients and opportunities in the consulting space.

As noted, I’m more comfortable with writing, so here are some pointers to posts from the past decade that cover similar ground to the conversation:

• First steps in data science: author-aware sentiment analysis
• The hardest parts of data science
• Is Data Scientist a useless job title?
• Ask Why! Finding motives, causes, and purpose in data science
• Advice for aspiring data scientists and other FAQs
• Defining data science in 2018
• Software commodities are eating interesting data science work
• The mission matters: Moving to climate tech as a data scientist
• Remaining relevant as a small language model
• Was data science a failure mode of software engineering?
• Lessons from reluctant data engineering
• Substance over titles: Your first data hire may be a data scientist
• LinkedIn is a teachable skill
• Learn about Dataland to avoid expensive hiring mistakes
• First year lessons from a solo expertise biz in Data & AI

If you found the chat useful or have any questions, please feel free to get in touch.