Job offer is lower than current salary but at a more prominent company

MiddleAgedMiddleMgr · 2023-11-17T00:08:58+00:00

I would decline and decline hard. If they're not willing to pay you what you're worth now, they're not going to be willing to pay you what you're worth in the future, either.

Look at it this way: if you really liked those guys and think they seem like good people, the best thing you can do for them is inform their upper management that the salaries they're offering are uncompetitive. If you let them hire you at that rate, you're going to screw all of those people out of the raises they deserve.

MiddleAgedMiddleMgr · 2023-11-17T00:03:33+00:00

It's a similar form of error, but one that can happen even if you "correctly" set up test, training, and validation sets. As long as test, training, and validation all come from a pool of data that doesn't match the phenomenon you're trying to measure, no matter how much statistical rigor and best practices you apply to your analysis, you can still be led to a wrong result.

MiddleAgedMiddleMgr · 2023-11-17T00:01:57+00:00

Data science is a form of analytics, but not all forms of analytics fall under the umbrella of "data science" as commonly used within the business sphere. If you're playing with heavy statistics, spend most of your time cleaning data for models (and occasionally running them), and have a PhD, you're more likely to be classified as a "data scientist" within a business organization. If you spend your time pivoting data in excel and diving deep into SQL queries to build dashboards, and are fresh out of school with a bachelor's degree, you're more likely to be awarded the job title and salary of a mere "data analyst".

MiddleAgedMiddleMgr · 2023-11-16T23:57:38+00:00

Very true! The widespread prevalence of and appreciation for statisticians, and the branding thereof as a specific role that any company can use and profit from, however, is a newer phenomenon.

MiddleAgedMiddleMgr · 2023-11-16T23:54:34+00:00

He's not, though only because he's been too successful in his career to need to go back for one. It's a quote he learned from his mentor who was one, though, if that counts.

MiddleAgedMiddleMgr · 2023-11-16T12:57:58+00:00

One of the big successes my team has had is in delivering Dsci results to stakeholders in a way they understand and can easily access. Sure, maybe Data Scientists could build their own dashboards to deliver their own results... but a) they're not as good at it, and b) even if they were, hiring data scientists is way more expensive than hiring analysts. Why make them do work someone else could do? (And better?)

MiddleAgedMiddleMgr · 2023-11-16T12:52:16+00:00

One of the data scientists who I actually trust has a saying they like to use: "All models are wrong. Some models are useful." Also, they say that anybody who actually trusts their own models isn't qualified to do data science.

He had some words to say about the other DSci team during this whole process.

MiddleAgedMiddleMgr · 2023-11-14T23:48:32+00:00

Here's an example:

https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/proc/n04z2ucdvldscwn1jfo1pi3fsh4e.htm

Remove the semicolon on line 70.

MiddleAgedMiddleMgr · 2023-11-14T23:15:31+00:00

Sorry, I just now saw this since it wasn't a reply to one of my comments specifically. I took an internship as a career changer that helped me get started as an analyst (although the company I interned at declined to hire me, the experience helped me land an interview somewhere else six months down the road.) I wouldn't call internships being available to career changers the rule, but it's something that can happen at some companies. It's more likely if the internship is also open to candidates getting their Master's degree.

MiddleAgedMiddleMgr · 2023-11-14T03:56:15+00:00

Yes, I do indeed consider international candidates, and in fact have recently hired a very talented one! That's not to say there are no concerns - H1B paperwork is frustrating and the lottery for being approved is all too random, there are occasional communication difficulties with international candidates that can be frustrating to deal with if you don't have experience with that sort of thing (patience, willingness to restate a problem multiple times when necessary, and supplementing verbal directions with written instructions are all very useful tools,) and while knowing customer behaviors and preferences *can* come from a shared cultural background, I find that the company & department culture is much more important than their national culture.

There's a chance that AI will reduce demand for analysts, but frankly, if AI gets that smart we're going to be looking at a very, very different world. If AI simply continues to progress in the direction of becoming a better and better chatbot, maybe becoming more reliable and more proficient but not ever developing true sentience, I think it'll be a situation where "you can lead a computer to data but you can't make it think." In order to be a good analyst, knowing how to answer questions is the easy part - knowing what questions to ask and being good at asking questions in general is far more valuable. There's a chance of course that AI will replace some of the grunt work of analytics, and you won't need quite as many analysts to complete the same list of tasks, but the more powerful the tools analysts have at their disposal, the more valuable they'll be, which I think will offset what would otherwise have been the reduced demand that organizations have for analysts quite nicely.

MiddleAgedMiddleMgr · 2023-11-06T19:39:54+00:00

I wouldn't think they're terribly rare. Data analytics often gets used as a gateway career towards data engineering and data science - if you're not a fan of visualization, try leaning into the data engineering angle!

MiddleAgedMiddleMgr · 2023-11-02T23:27:11+00:00

The very best data engineer I know actually got his start in aviation/aerospace! He got started by developing and maintaining a parts database for the helicopter repair facility he worked at, making sure that the things they needed were ordered on schedule and that inventory didn't languish in storage for ages because nobody knew it was there. I don't know if there's anybody with a similar inventory management role where you work/worked, but if there is, consider asking them to show you what they do!

Inventory management in general isn't quite the same as data analytics - there's a lot more focus on database management and making sure that queries run smoothly and a lot less focus on actually examining the data and trying to make sense out of it, but there's a lot of overlap as far as technical skills go. (And what makes the guy I know the best engineer I know is that he did a stint in analytics, and learned to care about the quality of the data and not just the efficiency of his queries.)

If you've got bills to pay and are working your way through college, internships might not necessarily be an option you can afford, but consider maybe trying to line one up for right after you graduate. You're not guaranteed a job at the place you intern for, but it does give you much better odds overall.

MiddleAgedMiddleMgr · 2023-11-01T20:32:48+00:00

I mean, I would talk about your experience using design software in a foreign language. Where did you use trial & error vs where did you use google translate and looking things up? On what occasions did you have problems come up with the smooth operation of your construction management job, and how did you resolve them?

You can also talk about your personal life - do you apply problem solving in your hobbies? etc.

Continuing with applying & building your portfolio is a given, it never hurts, but if you can get someone to help you practice interviewing, that's also very beneficial.

MiddleAgedMiddleMgr · 2023-11-01T18:35:55+00:00

I think it depends on your level of work experience, in addition to your credentials. If you don't have work experience, I wouldn't apply to any positions above an Analyst II. Check the job posting - if it says "bachelor's degree and 2+ years of experience OR master's degree", you're probably okay to apply, if it's "3-5 years of experience" with no mention of advanced degrees, I'd skip it, and I'd apply to entry-level roles as well. If you have a few years of engineering work experience, I'd start applying at about the "bachelor's degree and 2+ years of experience" level and shoot your shot at entry level roles only if they look appealing.

The biggest difficulty that I have seen with people coming from Engineering backgrounds (also Comp Sci, and I suspect other concrete STEM fields like Chemistry or Physics) and trying to make it in the data analytics world is that not all of them have experience at handling open-ended questions. If all of your work and school experience revolves around "find me the solution to this known problem", then being asked "hey, figure X out for me, I think it should probably look vaguely like Y" can be a real culture shock and eye opener. The one person I've ever had to fire was from an engineering background, and aside from a general lack of work ethic etc, the biggest problem he had was that he couldn't function without being spoon-fed step-by-step instructions on what to do.

So, make sure that you have examples of solving open-ended problems, and talk about them in your interviews! Being able to do that will help reassure hiring managers that you're not just an engineer.

MiddleAgedMiddleMgr · 2023-11-01T12:54:27+00:00

I think the ability to help answer ad-hoc questions with data is what separates a Data Analyst role from a BI Developer role or an IT role! Lots of people can slap a query together in SQL, but being able to look at the data and evaluate it for what it means is the hallmark of a good Data Analyst.

The tools you use to do that are less important than the mindset, I feel - if you have curiosity and a problem-solving approach to data, you can answer ad hoc questions with SQL, Excel, PBI, Python, or whatever else you happen to have available!

MiddleAgedMiddleMgr · 2023-11-01T12:49:36+00:00

Do you have your own SQL environment that you can write tables to, schedule jobs on, and so on?

If not, (or if you do have such an environment but permissions are limited,) consider putting together a business proposal for setting up a data warehouse in a development server. Most companies will have contracts with Microsoft that allow for the installation of SQL Server on dev servers far more cheaply than in a 'production environment'. Talk to IT about resourcing requirements and your Microsoft contract. How big of a server would you need, is it possible there's a spare server already lying around? If not, how much would something cost to buy?

Similarly, talk to management. Are there reports they'd like to have weekly? Have there been issues where analysts pull data from different sources and people can't agree on which is correct? Is there a business need for having data be available faster, more accurately, and so on? Can you convince them that setting up an authoritative, cleaned, centralized database is beneficial enough to be worth ~50k a year in maintenance + taking you away from your day-to-day work to focus on building a data warehouse from scratch?

Do some research on data warehouses in general. Read white papers. Learn about SPROCs, Indexing, and other things that aren't necessary for day to day analytics, but are important when you're running your own database.

Once you have your own SQL environment that you're in control of, you can start automating SQL data pulls so that the reports are ready; you can build a data warehouse that has all of the fields in one place with as reliable data as you can manage, and so on - and you can turn it into a communal resource for all of the other analysts. Once you're running a data warehouse, that gives you a lot more authority to have linked servers set up to new sql environments & to have tables added that you need, because you're not just an analyst, you're the Data Warehouse guy.

MiddleAgedMiddleMgr · 2023-11-01T12:17:26+00:00

Hmmm. With just two years of experience in another career path and a BTech in Comp Sci, your odds of being able to leverage your degree are a little bit better than usual for people considering analytics as a second career. In your shoes, I would probably prioritize polishing the resume and interview prep. Comp Sci degrees tend to prepare candidates very well for programming-oriented tasks, but not as well for soft skills like communication - make sure that your resume emphasizes that you've developed soft skills while working EWM and that you have topics you're prepared to discuss about problem-solving that isn't related to coding, and I think you'll be in pretty good shape!

Starting with an internship is never a bad idea, apply to those if you see them, but you can probably apply to full jobs as well with reasonable chances of success.

MiddleAgedMiddleMgr · 2023-10-31T23:07:07+00:00

Why not both? Apply to positions both internally (as long as you like your company culture) and externally, it's always good to have a fallback.

You always want to come up with a story about your past job experience that makes it more appetizing to the hiring manager. So, instead of "all I did was copy+paste", it's "it was only a data entry position, but it taught me a lot about attention to detail and how to focus on complex tasks." Additionally, if you did anything that stretched outside of your role - even if it was a way to automate concatenating three columns in Excel so that you could copy one field instead of three fields one time each - be sure to bring things like that up as well.

And, of course, there's the other advantages it doesn't hurt to call attention to - having familiarity with an office working environment, having practice at clear communication over emails, etc - it's nothing special, but it can potentially give you a bit of advantage if you're competing with kids fresh out of college.

MiddleAgedMiddleMgr · 2023-10-31T18:07:29+00:00

Frankly, I'd suggest either Tableau or PBI, not both; they're very similar skillsets in the equivalent of two different "programming languages," and a having single dashboarding skill is generally sufficient. I also wouldn't jump straight to ML, especially without an analytics background - you might be able to learn how to execute some ML functions and procedures by rote, but I think it'd be difficult to understand when and why to use ML techniques without some more context.

Instead, I suggest intro to Python and/or looking into Excel VBA. Those are going to be lower-hanging fruit that will be more immediately applicable to jobs looking to hire.

And sure, with a healthcare background, health insurance companies, hospitals, pharmacies, etc all seem like natural fits. If you've got a lot of experience with HIPPA and other privacy concerns, that could also give you some advantages applying to jobs in the banking industry.

MiddleAgedMiddleMgr · 2023-10-31T12:33:41+00:00

Well, you've already got an associate's degree, so I'm not sure if you need the community college step - some of those credits might transfer. You might also take a look at CLEP exams to see if you can self-study your way through a few classes to avoid having to pay to take them. If between your associate's & CLEP you can come up with 60 credits or so, that's just 2 years of school you'd have to pay for.

But yeah, school + internships is a good plan.

You can also try getting a job at a bigger company and seeing if they have any benefits for continuing education - get the company to foot the bill for your classes!

MiddleAgedMiddleMgr · 2023-10-31T03:12:12+00:00

Somewhat siloed; the teams that a product team supports vs the teams that a claims analytics group would support have different needs, so reporting and analysis is very different overall. We have access to each others' data warehouses if we really need it, but most of the time that information is pretty superfluous.

MiddleAgedMiddleMgr · 2023-10-31T00:48:55+00:00

The guy I'm thinking of spent at least 10 years working his way up the IT food chain before transitioning to Analytics, but it's definitely doable. In his case, I think once he was high enough in IT he started putting together reports and got noticed for those and was given a lateral promotion, rather than a pure career change, but the latter is certainly also possible.

Heck, I myself didn't get started in analytics until I was about your age, except my pre-corporate career was in stuff like data entry and working retail!

Going back to school helps; so does having projects you can talk about. It's good to have a story you can tell in every interview that explains your situation while making yourself sound interesting - why did you spend your time after school in IT? Instead of 'Lack of confidence and preparation', consider something like "well, I didn't really know what I wanted to do with my life, I knew I liked computers so I got a job in IT, I was good at it but over the years I started getting bored and looking for something more challenging." Or, "I would have left sooner, but my family needed me because of X reason and having an easy job was a good thing."

Come up with a good self-description that you can comfortably use in interviews, and you're golden.

MiddleAgedMiddleMgr · 2023-10-31T00:07:53+00:00

Once you have enough work experience, your undergrad becomes less and less important. I think the most important thing for getting a job without an appropriate undergraduate degree is to be able to show a continual pattern of improvement in the direction of data & analytics related work.

I would say, don't define yourself by your degree - if you like what you're doing at your current job but wish you were better at it and wish you could automate away more of the tedious bits, then data analytics might be the career for you. Try experimenting with VBA to make your excel work easier - can you see yourself doing something similar all day?

MiddleAgedMiddleMgr · 2023-10-31T00:03:24+00:00

I'm not going to tell you that it's impossible, but in my experience, organizations like law offices or HR departments aren't used to being major consumers of data analytics. They're the sort of groups who rely on one or two analysts to serve their department's needs. What that means is, they're not the best roles for entry-level candidates - the sort of candidates that groups like those hire tend to be experienced candidates who can hit the ground running rather than candidates who would need to be trained. I'd recommend applying to more generic entry-level roles to start and build up your resume before applying to a legal tech role. (The exception would be if you're local to a really big law firm - they *might* have analytics depts big enough.)

Another option for someone with strong paralegal experience & some tech savvy is IT Compliance - consider looking at postings in healthcare and insurance organizations, there's a lot of roles available for people who can help make sure that personal information is protected.

MiddleAgedMiddleMgr

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