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PaperclipPerfector  5mo ago (text post)   2368 thread views

Wellness Wednesday for November 8, 2023

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Was just offered a data analyst position, specifically in Revenue Management, starts January. Will be my first data job, transitioning from software engineering. Will also be my first in-person corporate job as I've been working from home since the pandemic. Any advice? I'm pretty good with SQL and the Python ML libraries, also setting up basic data pipelines in the cloud. But I don't know anything about Revenue Management.

I'm an early career Data Scientist in e-commerce and logistics so I might not have the best advice, but maybe the most recent advice.

  1. You know pythons ML stack and sql already that's starting off on a good foot. I don't think you'd benefit much learning more technologies. Most BI tools are the same if you know how to query using sql and for the really dirty stuff you can just pull out pandas and create a report in a python notebook.
  2. Learn the domain well. Can't really help you much there. But this is non-negotiable. You will be struggling a whole lot if you get some numbers and you second guess yourself if these numbers pass the smell test or not.
  3. Don't skimp on statistics or more generally just math like a lot of developers do. As an analyst, you probably won't need to be a Statistics God, but once again knowing your stats 101 really well will save you from a lot of second guessing and awkward moments. Here's an anecdote when it saved me a lot of trouble. We track the 90th percentile timings for a lot of things. One of the higher ups was confused at to why 90th percentile of A + 90th percentile of B wasn't equal to 90th percentile of A+B. Well because it's not a linear function and that property doesn't apply. I was asked to investigate why "things weren't adding up". Knowing some basic math saved me a lot of time/headaches, and continue to do so.
  4. Know how to answer basic questions. "What was our revenue during Q3 for location X only during the every alternate weekends" should take you like 3 minutes to answer. Boy oh boy is there a lot of confusion because simple things like this that can be looked up don't get looked up. Don't take anyone's word for anything. Just look these things up.
  5. Know how to answer the hard questions. Lack of information is... information. There's a reason we can drop the nth column when one-hot-encoding without any information loss. Because the counterfactual is information! Basically don't think like an SQL monkey and limit yourself to things that only the BI tool can answer with its GUI. There are lot of opportunities for questions like this in logistics. There exists no columns in a database that allows one to catch warehouse workers slacking off, items going missing or items not being checked in, etc, but a lack of certain rows tells me where to look. So keep an open mind towards the data it probably has some secrets there in plain sight. I'm sure there are lots of things like this in Revenue Management as well.

They should pair you with a functional expert who knows about Revenue Management. Even if they don't I wouldn't worry too much. Revenue is pretty cut and dry quantitative, so you're probably going to have straightforward requests without much room for interpretation.

The upside for all data roles -- forget having to write real tests! Just run the damn pipeline / model / whatever. There is no integration, everything is a one-off.

Until you get a VP-level (or higher) who builds their promotion case on creating the Integrated Data Infrastructure Omniscient Technology and starts to require really strict sprints and CI/CD pipelines ... for building reports.

But, for the time being, if you've actually written code in a real SWE environment, data work will be technically less rigorous, but with the potential for more back and forth with human principals.

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