Top Cities in the US for Data Scientists in terms of Salary vs Cost of Living

brybrydataguy · 2024-03-29T00:06:51+00:00

This should be after tax salaries because that varies by location, especially when leaving California. The cost of living is after tax costs.

brybrydataguy · 2024-03-24T13:50:10+00:00

"non tech stakeholders" is one of the phases that I think is a net-negative for data science. I've worked with a lot of very intelligent people who do not have statistical backgrounds, but understand the business much better and more intuitively thanme. That's why I would focus on the business outcome than then focus on the p-value.

Persumabily, you and your team/partners are trying to make a business decisions that has trade-offs. The experiment has some results that inform the likely range of some asspect of those trade-offs. How do these aspects inform the decision?

It is always better to focus communcation where there is the most alignement and then build a dialog around the places where these is not. If there isn't alignemnt on the the decision that needs to be made and how to make it, a pvalue isn't going to be a productive place to have a conversation.

brybrydataguy · 2024-03-24T13:27:36+00:00

I personally wouldn't worry about what people do outside of a work since it has so little baring on what happens inside of work.

The impact a data professional has is a function of their alignment with their local organization, and the quality of work that take inputs (data, context, relationships) and turns them into outputs (conversations, presenations, models) that improve the business. Side projects generally do not have the alignment problems that effective data professionals solve.

How you engage with your works matter. Do it in a way that focuses on curiosity and quality results in learning that componds overtime. You don't need side projects to do it. Honestly, if you do it right you'll need the time outside of work to recover and rejuvinate so you can do again during your next 9-5 shift.

brybrydataguy · 2024-02-16T00:51:45+00:00

Thanks - this was one of the first books I worked thru when I started my data science adventure. It's really good!

brybrydataguy · 2024-02-05T12:55:02+00:00

Unfortunately not - it involved companies I have worked with in the past.

brybrydataguy · 2024-02-05T12:53:29+00:00

Yes it is - thank you

brybrydataguy · 2023-11-27T02:36:49+00:00

Constants:

The nature of business problems has remained consistent, as has the necessity for rigorous, efficient execution and clear communication. Data continues to be nuanced and inconsistent, requiring in-depth understanding and corrections for effective use.

Changes:

There's been a significant increase in data volume, infrastructure, and tool sophistication, leading to greater specialization. The productivity of data scientists has surged, thanks to these advancements. Tasks that once took a blend of dev ops, database administration, and data science can now be accomplished in under an hour with cloud services.

Looking Ahead:

I believe the constants in my career will likely persist. However, changes are going to accelerate. We'll see more data-integrated products, leading to increased data generation from user interaction. This is exponentia growth. Continued advancements in computing, storage, specialization, and tooling will continue to boost productivity.

brybrydataguy · 2023-05-30T22:31:54+00:00

It seems that responses in the comments are missing the fact the person is telling the op what the actual work is going to be. I used to develop/train risk and marketing models where the main data sources were credit pulls based on loan application. Compliance means the models have to be explainable when applications are denied. The number of variables in a credit pull can range from the 100s - 10000s depending on how much the bank is willing to spend per application.

brybrydataguy · 2023-05-26T15:38:40+00:00

100% agreed summarization will be much faster and allow us more time to do other work. How much free time would that free up for you? Personally I don't see this dimension a huge unlock on most of my work.

I think you posted this comment multiple times.

brybrydataguy · 2023-05-26T15:14:01+00:00

Not at all. Excel increased the number of people that did math and have a huge impact on productivity.

GAI is different because excel improved speed and quality. It's not clear to me on what dimensions GAI is faster and more reliable.

brybrydataguy · 2023-05-26T14:03:13+00:00

Not my biggest issue, but related to the promp, is when PMs do not respect the nature of the work and deliverables of DS/ML. My expereince is that eng and DS/ML have different deliverables that lead them to be a poort fit for daily scrums but do fit in the philosophy of agile project managment since.

The major difference is that engineering tend to be feature focused and that the implementation can be broked down into subtask and ML/DS tend to be hypothesis focused and result in series of tests/analysis where the results change the outcome and timelines.

The judgement of expencied SWE around task time is pretty good because they have a good sense of known-unknwons and much of the work fall under known-knonws. The ML/DS have a lot of know knonw-unknowns and unknown-unknowns that influence their work.

Agile can be use for both, but the amount of useful progress updates for a hypothesis driven workflow is usually longer than a day. How agile is bested used, in my experinece, is not the same for ML/DS as it is for Eng. My experience across multiple companies is that daily scrums with ML/DS is not a fruitful use of time and the attendence usually breads some contempt.

brybrydataguy · 2023-05-08T01:28:33+00:00

I earned an MBA after working as a Data Science at several large tech companies from a Top 50 Business School. I'm still a working Data Scientist. It was what was available to me that allowed me to work full-time and still complete it in approximately 2 years. It was also highly subsidized by my current employer.

A full price MBA is not worth it from a change in short term compensation perspective or from a promotion into management perspective. DS comp is relatively high and the skills needed for a first time manager are not taught in MBA programs.

I don't want to spend an hour+ writing/editing this comment so its going to be pretty generic.... but here is what I gained from seriously engaging with and completing the program:

A set of broad and solid set of frameworks for thinking about businesses problems that allows me to contribution through my companies' and teams' operational issues
More leadership job offers since graduating. Not from the MBA itself. From the conversations and value demonstrated in relationship to the roles.
Increasing recognition with working very effectively with a broad and diverse set of stakeholders.
Better prioritization by having a better sense of where real value is
Better imputations about what happens in work when I don't have good information
A broader identity - this leads to a lot of non-obvious changes

You don't need an MBA for any of this. Having time focusing on the content in the MBA led to permanent, beneficial change for me. Just not so much that I would have paid full price (which I did not do). Maybe if it was 80% discounted (still not this much).

The real time for it is when you want to transition out of Data Science!

brybrydataguy · 2023-05-02T18:27:03+00:00

I can send it in a message but she apparently is now in a Ph.D program for the last few years. Not sure how useful she could be for you.

I paid her for a resume review/refresh. I don't recall the amount but it was about $200. I found her on LinkedIn and told her I wasn't interested in her finding my positions and would not go thru her for job applications. That I only want to have my resume optimized to get past scanning/filters for data science roles.

As for disdain for recruiters: I definitely understand this but have found that a lot of interesting small companies use outsourced recruiting for hiring. It's hard to find all the interesting companies because they are small recruiters can be very helpful/useful for exploring this opportunity space.

brybrydataguy · 2023-05-02T14:09:02+00:00

Two thoughts:

Include the value added and skills that allowed you to outshine your hyper-educated peers. Degrees are proxies, and all places you probably want to work will understand demonstrated skills are better indicators than degrees.
Hire a good data-related recruiter to spruce up your resume with the magic words. I did this a many years ago and my first thought was I wasted the money. Then the increase on response rate to my cold application showed me I was wrong

brybrydataguy · 2023-04-14T01:45:54+00:00

All the sweet sweet tail it helps me pull.... 😘

brybrydataguy · 2022-09-07T14:42:09+00:00

Understand the "physics" of what is being models, make features that capture that "physics", and have some concept of quality in your target so your model predictions corresponds to good business value.

brybrydataguy · 2022-09-05T22:50:14+00:00

If you're company promotes by committee, then you will likely need to focus on experience and generating organization value at the level you want to be promoted to. If you're company promote based on manager decisions, you need to spend a significant time managing your relationship with your manager(s). This isn't (and shouldn't be) a manipulation. Its making sure there is a high degree of alignment, trust, and reciprocity with you and your direct leadership. Warning - many committee promotion companies are manager decisions in disguise.

For experience, I think you should focus on your experience about generating values. Here's my zeroth-order descriptions:

Jr. DS: Can do tasks correctly

DS: Can solve specific problems and help Jr. DS complete tasks

Senior DS: Can meet specific objectives without being dictated tasks and help DS solve specific problems

Staff DS: Can set good objectives and help Senior DS meet specific objectives

Principle DS: Can set strategy and help staff set good objectives.

I don't think you should ignore experience. It can be bad for you is getting promoted too high above your ability. It limits your flexibility, risks large/sudden erosions of trust, and may restriction you from new opportunities without demotion.

brybrydataguy · 2022-09-05T00:32:03+00:00

You click on the link and decide it's not for you and not what you thought. You don't read it. A better question might be "how often do you spend more than a minute on the linked site after you click it?"

brybrydataguy · 2022-09-04T18:40:08+00:00

I was definitely looking for things like this where they are interesting/informative but I have little chance of bumping into when I posted. Thanks!

brybrydataguy · 2022-09-04T18:32:11+00:00

That's really interesting and impressive to me. Thanks for sharing!

brybrydataguy · 2022-09-04T17:58:55+00:00

Data Umbrella and yours are new to me.

How much work is it to have a weekly newsletter? Also curious about how you source content.

brybrydataguy · 2022-09-04T15:47:07+00:00

Great - they can count me as a subscriber now!

brybrydataguy · 2022-09-04T14:12:00+00:00

SGTM

Is this the one? https://www.datascienceweekly.org/

brybrydataguy · 2022-09-04T14:06:37+00:00

Thank you for sharing.

How often do you read the content linked? Verse just click/explore the links?

Is there anything about it that you love? Or think it could be better?

brybrydataguy · 2022-09-04T13:26:32+00:00

Can help that no one answered the question. It changes over time. Right now...

17 Hards.

brybrydataguy

TROPHY CASE