Dan Gets Laid Off, Oscar Quits (4/5)

Previous: Oscar, The Data Science Consultant [3/5]

Among all team members, Oscar and Dan would be the most impacted by the outcome of the ongoing data science project.

Oscar, the data scientist, does not have years of data science and machine learning experience. This is his second data science project at his second job within a year. His first job could last for 9 months only. However, Oscar is smart enough to realize that if the project fails, which is very much likely, he would be the person to be blamed the most.

Dan, the project lead has been at the company for half a decade. He understands company culture well. In past, he has seen the consequences of not meeting critical goals. The objectives of this project are definitely linked to the company’s critical goals, and Dan knows that very well.

The project is not in a good shape after Ryan’s departure, and that thing is keeping both Dan and Oscar up at the night. Given the sensitivity of the situation, both have brushed up their resumes. They have just started looking at job postings during the breaks.

Oscar has almost half a month to train a machine learning model. The challenge is to train one with acceptable prediction accuracy. He has been working really hard by scarifying his evenings, nights, and weekends. He has tried to leverage his knowledge, advice from professional connections, and online help to find a meaningful set of features to try out different learning algorithms. Despite all efforts, he is unable to see convincing results. As a final attempt, he reaches to one of his mentors, an experienced machine learning scientist, to get her advice. He shares the method and the results with the mentor. It turns out Oscar’s problem is not the selection of a proper learning algorithm or lack of good features but enough training data. So, the mentor has advised him to get more training data and fine-tune some of the features.

As time passes by quickly, and Dan is mounting pressure on Oscar. Though both constantly chat on the company’s IM, Dan has set up an afternoon touch-point meeting with Dan. In the meeting, Oscar would get to ask questions starting with “how long …”, “when would you...”, “why is it ...” etc. Less than a week is left, and Oscar is planning to share his final feedback and progress after meeting with his mentor. Oscar schedules a 1-hour 1:1 meeting with Dan. It is going to be a tough meeting, Oscar knows that.

Oscar has put together a couple of slides to show the results along with the findings such as: - the accuracy of trained models is very low - insufficient training examples - the need to invest more time in feature engineering - the team should have done a pilot or POC project before kicking off this project.

As Oscar shares his findings, the whole thing seems to be a bombshell to Dan. Though Dan had a sense of the challenges Oscar was facing, he was not expecting a complete failure. The meeting didn’t end up well. After that meeting, Oscar was spending most of his time applying for jobs and speaking with recruiters. At university, he has worked on a computer vision problem as part of his master’s thesis. A startup in the town has a job opening to work on a similar computer vision problem. He has already spoken with the director of technology. Next, he is going to speak with the VP and president of the company.

Dan is preparing to present the status (RED) of the project to his leadership. Dan is not applying for jobs actively. However, he has started reaching out to his former colleagues working at other companies. When Dan presented the project status; clearly indicating the impossibility of delivery anytime soon. The leadership knew about Ryan’s departure and the roadblock of the machine learning component. However, they never thought this component is the lifeline of the entire transformation project. The company has spent so much money and time on this project. Other business units were gearing up to align with upgraded AI-driven business processes. As the failure gets reported to the CEO of the company, the heads start rolling and one of them was Dan’s. Around the same time, Oscar gets the offer from a start-up and decides to leave immediately.

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