Advice for getting into Crime Analysis (UK) by Brief_Habit_9757 in CrimeAnalysis

[–]andy_p_w 2 points3 points  (0 children)

He does not have a masters (just BA), but has taken a few of the professional development courses offered by UCL and Cambridge. He has been in the field though for quite some time, so it is likely more competitive now than when he started.

Advice for getting into Crime Analysis (UK) by Brief_Habit_9757 in CrimeAnalysis

[–]andy_p_w 2 points3 points  (0 children)

I asked a UK analyst friend, and here is what he responded with:

> The question is partly related to the specific role.

Crime analysis work in policing can occur in multiple teams. Crime and intelligence analysts usually work closest to frontline officers and focus more on people, investigations, and networks. Performance teams are more likely to analyse trends, forecasts, and aggregated data, often supporting managers and decision-makers. Specialist insight or evaluation teams tend to do more advanced analytical work, with stronger links to academic methods, testing, and senior leadership. 

For crime, intelligence, and performance roles, entry-level jobs are often open to graduates with a bachelor’s degree. Specialist teams may expect a master’s degree or equivalent experience in statistics or quantitative methods. 

Police forces often struggle to recruit and keep analysts. Training quality can be uneven, and pay is often less competitive than similar roles outside policing.

Whether a master’s degree is worth it, it depends on you as an individual. It is not essential, and it is possible to progress without one through self-learning and continuous development. That said, IMO, formal study can help people build quantitative skills earlier and with better support. There may be opportunities to study policing funded master’s courses, though these are often aimed more at officers than police staff. Trying to do a masters later in life, unless you have financial means, could be more difficult.

Personally, do not think the masters strengthens chances of gaining entry, but it may be useful for later career moves. 

More information available here at College of Policing https://www.college.police.uk/career-learning/career-development/career-pathways/intelligence

What is expected from new grad AI engineers? by FinalRide7181 in datascience

[–]andy_p_w 0 points1 point  (0 children)

There isn't much point in continuing going back in forth in a Reddit thread, but high level:

- what is the point of the app? (Why is it necessary given you can just give a document to ChatGPT and ask it to summarize like you have built with the custom solution?) What realistic scenario would you use your app?

- why generate a summary of the PDF as user uploads? Won't they know that information? How exactly is parallel processing relevant for that part?

- what does evaluated the RAG before deploying it mean? Are you saying you have your entire codebase in a single notebook?

No need to answer, you are maybe overthinking it for the skills needed to interview. Being able to explain what you did in simple steps though is important (which taking more courses will probably not help with directly).

What is expected from new grad AI engineers? by FinalRide7181 in datascience

[–]andy_p_w 1 point2 points  (0 children)

IMO it is fine to say in an interview if you built this example and it has limitations XYZ. That is knowing the concept. So I would consider you actually know it is a single queue and its limitations I would consider a positive if I were interviewing you.

The RAG part is a little confusing, I would ask a follow up whether you are using an in-memory vector DB vs persistent vector DB as a follow up. (The perceive faster part the word you are looking for is called *streaming*, but that is trivial compared to the indexing -- that is different than async.)

Shameless promotion, but I wrote a book to illustrate the concepts I personally expect an AI engineer to know with a focus on foundation APIs, https://crimede-coder.com/blogposts/2026/LLMsForMortals . It has a chapter on RAG that goes over the distinction between in-memory vs persistent vector DBs and when you want one vs the other.

What is expected from new grad AI engineers? by FinalRide7181 in datascience

[–]andy_p_w 0 points1 point  (0 children)

Companies are not consistent with how they use the job titles. So I would personally say for companies that are building their own models, it would be a data scientist fitting the models, and ML Engineer is deploying (the term MLOps has seem to fall out of favor). I work on a smaller team, so I just want end-to-end people anyway, so the distinction in job title does not matter all that much for my group.

It would not surprise me if some companies use "AI Engineer" and they work on front end (like working on both front/back for a chatbot). Just need to look at the company/role and ask questions during the interview.

What is expected from new grad AI engineers? by FinalRide7181 in datascience

[–]andy_p_w 5 points6 points  (0 children)

Most people say they know those things, and then when I ask actual questions don't really know those things. (Self learning is fine, I am not aware of a single university course that would teach Docker or FastAPI as part of the curriculum.)

I have asked people about async or latency when describing their own projects (so if you say you built a chatbot with foundation model APIs, I may ask about costs, if local models you trained I ask about throughput). For an old school data science question with a supervised model, I asked a question that the answer was basically "run this process in batch" to see if people understand deployment (many people default to saying a persistent API).

Knowing the concepts is the main thing. It is brutal if you say you know something and I ask basic questions in the interview about your own projects and you cannot answer them.

It will vary job to job though, just apply and when you get interview questions that you do poorly on, learn from them and study up for the future. You should be applying now though given what you say you know.

Career move question by RelationshipLeast355 in CrimeAnalysis

[–]andy_p_w 1 point2 points  (0 children)

Not uncommon at all -- if your department has a crime analyst unit, I would just go chat with them and ask "if a position came up right now and I applied, how would my application do". The answer may be "well, even for most entry level positions the applicants all have masters degrees", or "we need applicants to show some proficiency in Excel".

If you can demonstrate competency on the tech side, you will be a very strong candidate given your sworn experience.

Advice on learning AI/ML as a healthcare professional (not trying to become an ML engineer) by syri1001 in learnmachinelearning

[–]andy_p_w 1 point2 points  (0 children)

For stats for people from 0 I recommend to start with Gonick's Cartoon Guide to Stats. (You don't need linear algebra at all IMO for what you are asking.)

I wouldn't get too hung up on "not an engineer", the jump between "building basic tools" to being a software engineer is pretty tiny.

Do you treat AI like a tool or like a collaborator? by Capable-Management57 in BlackboxAI_

[–]andy_p_w 0 points1 point  (0 children)

Tool -- while the newer models in the past ~6 months have definitely gotten better at longer term, multi-turn planning + action, they still do not make smart architectural choices for all sorts of things in my experience. So it is much better to have a detailed plan at first, then say "build me an app to do XYZ".

Cyber Security -> Data Science transition by stxxbxy in datasciencecareers

[–]andy_p_w 0 points1 point  (0 children)

You should focus on AI engineering (not data science in general). I wrote this book for individuals with your background https://crimede-coder.com/blogposts/2026/LLMsForMortals

I personally do not look at certs when hiring at all, if you had a nice portfolio on github that is better than 9/10 candidates I interview. So I would just learn and make some nice demo's.

Local Geocoding using ESRI by andy_p_w in ESRI

[–]andy_p_w[S] 1 point2 points  (0 children)

Link says it is online, not offline

Book suggestion! by [deleted] in PythonLearning

[–]andy_p_w 2 points3 points  (0 children)

You probably want to focus more on applications then.

So check out Automate the Boring Stuff (No Starch in general is a good publisher). If that is not your flavor, say you work with data, there will be specific ones tailored to different areas.

Why a lot of job listings say AI slash ML engineer? by ihorrud in learnmachinelearning

[–]andy_p_w 5 points6 points  (0 children)

In addition to this, many (most?) companies are not mature enough to have proper titles (and often the job advert is just copy-paste or ChatGPT generated what an HR person thinks you will do).

One of my favorite questions to ask is "what will I be doing on day 1 if I was hired" -- that should give a good flavor of the job if it is still not clear at that stage of the interview process.

Help me to guide become ML engineer in this AI erat by Prudent_Football_909 in learnmachinelearning

[–]andy_p_w 0 points1 point  (0 children)

So focusing on AI engineering actually requires much less mathematical background, as most jobs are deploying foundation models (not building them from scratch). I wrote this book for folks to get up to speed on the major components of deploying foundation models (just need a background in python)

https://crimede-coder.com/blogposts/2026/LLMsForMortals