Simply stating it doesn't give an indication of merit. "I was accepted to do a three month, self-directed retreat for computer programmers"īeing accepted sounds like it was prestigious but you don't tell why. "The Project Bot will be available to download in the online Slack store"Ĭompletely irrelevant to your experience, it sounds like you're trying to sell your project and not your experience. This is the Situation of STAR, there is no Action or Result Using Jira seems important, but participating in stand-ups doesn't speak to your experience. "Used Jira and participated in weekly stand-ups, demos, and retros" This is a Situation of Star, there is no Action or Result What discernible skill does the provide to your experience? "Worked with Tech Lead, Project Manager and Client." Here is a list of sentences that give your document absolutely no value and why or what the reader is thinking when reading them: This document is you writing it for yourself and not a third party perspective. I would love to hear your take on this dilemma.You spend a lot of time describing what you did and give absolutely no indication of how you performed or what the result of your actions were (this is the "A" and the "R" part of the STAR method). However, it's unlikely they would really understand what the problem was and what my solution did. This means that the skimming recruiter will catch a lot of ML-specific jargon. If I go with a more one-liner approach, the keyword-density definitely increases. If I go ahead with the STAR format, I have a resume which is easy-to-understand (at least if it's done right), but the word count is high and there's a good chance an overworked recruiter will skim and decide against going forward. My fear is that the recruiter would eye-ball the 2 pages, see very few "keywords" like "Random Forest" or "LSTM" and would reject the resume. Due to the verbose format, "ML jargon-density" is very low. I have little hope that a recruiter would take the 5 minutes necessary to read and understand my experience. For instance, reading what I have written, it's easy to understand what the data sort-of looked like due to the examples of the features.ĭue to a large number of projects, the 2 pages now look extremely wordy. This lets me flesh out a very high level summary of what happened. Some industries, like Finance or Healthcare, have some very dense jargon which will never make sense to someone not intimately familiar with the industry.Īn ML project involves a lot of steps. That is a toy example chosen for ease of understanding. In the above example, exposition about what click-fraud is seems unnecessary. It's easy to wrongly assume people are familiar with the problem or even the industry. Is this too verbose a format? Some of my reasons for this format: The model achieved a positive predictive value of 70%, resulting in savings of $100M a year. I used an Isolation Forest model, leveraging features like click-velocity of user, time-of-day, User's IP etc, in order to identify a fraudulent user before they have caused too much damage. I worked with a major Australian ad platform in developing a click-fraud detection model. ![]() Fraudulent users try to subvert this model, by building automated programs that mimic a genuine click from a genuine user. I would frame it using the STAR approach:Ĭlick-Fraud Detection: Online ad platforms earn revenue when users click on an affiliated ad placed in a website, sharing part of the revenue with the website owner. I built an anomaly detection model using isolation forests to identify click fraud, thus saving $100M a year I am personally a fan of the STAR format for interviews or for my resume.įor example, instead of saying something like:
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