We talked about:

Tatiana’s background Going from academia to healthcare to the tech industry What staff engineers do Transferring skills from academia to industry and learning new ones The importance of having mentors Skipping junior and mid-level straight into the staff role Convincing employers that you can take on a lead role Seeing failure as a learning opportunity Preparing for coding interviews Preparing for behavioral and system design interviews The importance of having a network and doing mock interviews How much do staff engineers work with building pipelines, data science, ETC, MPOps, etc.? Context switching Advice for those going from academia to industry The most exciting thing about working as an AI staff engineer Tatiana’s book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/tatigabru/  Twitter:  https://twitter.com/tatigabru Github: https://github.com/tatigabru Website:  http://tatigabru.com/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

We talked about:

Tatiana’s background 12 career hacks and changing career Hack #1: Change your social circle Hack #2: Forget your fears and stereotypes Hack #3: Forget distractions Hack #4: Don’t overestimate others and don’t underestimate yourself Hack #5: Attention genius Hack #6: Make a team Hack #7: Less is more. Forget about perfectionism Hack #8: Initial creation Hack #9: Find mentors Hack #10: Say “no” Hack #11: Look for failures Hack #12: Take care of yourself Kaggle vs internships and pet projects Resources for learning machine learning Starting with Kaggle Improving focus Astroinformatics How background in Physics is helpful for transitioning Leaving academia Preparing for interviews

Links:

Mock interviews: https://www.pramp.com/ Learning ML: https://www.coursera.org/learn/machine-learning and https://www.coursera.org/specializations/deep-learning Python: https://www.coursera.org/learn/machine-learning-with-python  SQL: https://www.sqlhabit.com/  Practice: https://www.kaggle.com/ MIT 6.006: https://courses.csail.mit.edu/6.006/fall11/notes.shtml Coding: https://leetcode.com/ System design: https://www.educative.io/courses/grokking-the-system-design-interview Ukrainian telegram groups for interview preparation: https://t.me/FaangInterviewChannel,  https://t.me/FaangTechInterview, https://t.me/FloodInterview

Join DataTalks.Club: https://datatalks.club/slack.html