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Faced this weird behaviour from Optum recently. Gave interview for Data Scientist position. HR said feedback is positive. Asked for documents. It's been month now since I have shared the documents. I have no update on the offer. Today I called HR, she called me back saying the position is on hold due to recalibration in team, She has shared interview feedbacks to other teams and will get back to me in couple of days. I am clueless now. My last working day is approaching (In a month). Any Help??
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I have been given a verbal offer of 145k base for a Sr. Associate role as Data Analytics Lead. The offer doesn’t come with any kind of bonuses or stocks.
I have a Masters and 5+ years of exp and my current comp is 115.5k + approx 5k bonus + ~20k in stocks (yearly refreshes) which makes my total current comp to 140k.
Is it worth taking the offer for increase in just the base?
What are my chances of negotiating into a VP level role with at-least 155k?
#data #dataanalytics #datascience #Jpmc JPMorgan Chase
Anyone here who left fed job and regretted it?
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Product Management at a large bank vs Business Analytics/S&O for FAANG? Recently started in the former role, but have interview calls for the latter just come up in my mailbox. Similar comp when adjusted for the different job locations. Can anyone help me with the Pros and Cons please. I know the roles are different, and so are the industries, need to understand difference career paths and difference in corporate cultures. JPMorgan Chase Google LinkedIn Citi
Hi All, My sister has done Computer Science engineering Bachelor degree and has 5 years of work experience in India. She is applying for MBA at https://www.kenan-flagler.unc.edu/programs/mba/full-time-mba/ and https://kelley.iu.edu/programs/full-time-mba/academics/majors-minors/marketing.html. Her overall goal is to get into Software Product management. Any suggestions if any of these MBA’s can open path in the desired space or if she is better of doing an MS in Comp engg. to further develop deeper Technology skills. Thanks
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Quant lies/misses context. I’d consider the questions asked and the qualitative insight provided and as long as the context/question was clear go with that
Quantitative data is usually more accurate IMO.
A lot of people have a “feeling” about how users behave or about the importance of certain features, but the actual usage data will tell you the truth.
I had a feature B that paired with feature A and was told that I couldn’t launch a refreshed version of feature A without also releasing feature B at the same time because they were so important and “always” used together. The data showed that Feature B was only used with feature A around 5% of the time. I refreshed feature A and had 60% adoption of the new version over the next 3 months until I finished feature B. If I had listened to my qualitative data, I would have missed out on a lot of adoption during the busiest time of the year. My boss was happy I “trusted my instincts and followed the data” over all of the scary stories that clients told us going into the effort.