Hi Nina, thank you so, so much for sharing this post. I especially appreciate the framework you provided at the end, which is so cross-applicable to different domains -- I'm using it to map out the skills needed to excel in climate finance :) I had a question, however. Say one has developed a lot of depth in a certain area but is pivoting to a different field that requires a different area of depth. How can they leverage the depth they've accrued in this area, such that their investment isn't wasted?
Hi Rohan! Thank you for your feedback and comment - I totally agree that this framework extends to other fields as well. To pivot between fields, it is really helpful to think of the transferable skills between fields.
For example, if you worked in climate finance and then you wanted to go into accounting, you can leverage your finance background to build off of and grow into accounting as the two are quite related. It can be challenging to do so if the two fields are very very different.
How can a Data/Software Science Engineer effectively integrate the T-shaped framework into their daily workflow, particularly when balancing the need for deep technical expertise in areas such as machine learning and data processing, with the broader skills required for cross-disciplinary collaboration and effective communication with stakeholders?
Hi Nina, thank you so, so much for sharing this post. I especially appreciate the framework you provided at the end, which is so cross-applicable to different domains -- I'm using it to map out the skills needed to excel in climate finance :) I had a question, however. Say one has developed a lot of depth in a certain area but is pivoting to a different field that requires a different area of depth. How can they leverage the depth they've accrued in this area, such that their investment isn't wasted?
Hi Rohan! Thank you for your feedback and comment - I totally agree that this framework extends to other fields as well. To pivot between fields, it is really helpful to think of the transferable skills between fields.
For example, if you worked in climate finance and then you wanted to go into accounting, you can leverage your finance background to build off of and grow into accounting as the two are quite related. It can be challenging to do so if the two fields are very very different.
Hope this helps!
How can a Data/Software Science Engineer effectively integrate the T-shaped framework into their daily workflow, particularly when balancing the need for deep technical expertise in areas such as machine learning and data processing, with the broader skills required for cross-disciplinary collaboration and effective communication with stakeholders?