Submitted by doctorjuice t3_10dlogr in MachineLearning
I have around 6 YoE doing MLE full time work for various companies. Starting to get tired of working for these big companies and would prefer trying some freelance work.
Where are some websites or places I can get started? I’ve seen UpWork, but this seemed more suited for quick one off, software work and less for complex ML tasks last time I was on there (tried that several years ago in 2019).
I’m a consultant, and most recently was running the AI wing of a publicly traded consulting company as a full time employee , and OPs feedback is entirely correct.
I’m currently on a career break and returning as an independent consultant
Completely agree, I would also add just a couple of tips I've found (not directly related to the question):
- have a good circle of friends in ML you chat to, you will sometimes find yourself in places where you are the only ML expert and you will need people you trust to bounce ideas across/tell you about new things you missed
- invest in a decent workstation and if you can't afford one try to get your first gig where it's not needed and buy one as soon as you can. This has saved me tons of time over the years
- get good at showing results quickly to stakeholders... and I mean you should be able to hack it in an hour at most. They are paying you a lot of money and want the feeling of progress. Something in slides/powerpoint is OK, a dumb streamlit/whatever app is even better. Impressing a stakeholder is the best way to get repeat work
Ex-software freelancer here.
> First thing is that as a freelancer you're not part of "the team". This can be good or bad for you, I think it's fantastic.
Agreed, but with a few caveats:
You always need a plan for your contract to end, including early. (Never happened to me, but I always planned for it).
Companies will eventually try to treat you like staff: assuming you'll always be there and they can tell you what to do rather than asking if you'll do something. At this point you need to start telling them about the break from them you are about to be taking.
> In my experience most small companies won't have use for you. For one, you'll be more expensive than their employed staff, but they also want to keep that know how in house.
Disagree here: small companies struggle to get a wide enough set of skills, and they also have projects that need finishing without expanding their committed outgoings.
There are two major downsides to freelancing:
Location. If you are not in a very big tech city you will have to frequently relocate, or work primarily from home (in which case you are competing with very, very cheap people).
Skills. Companies do not give you time to learn the next big thing. You are expected to turn a profit for them from day 1. If they are going to be investing in their staff learning new things, it will be with staff they expect to stick around.
> Another thing to keep in mind: Do not go into this for the money. If you factor everything in: Vacation, sick days, hardware, licenses, pension/retirement (rule of thumb: 30% of your net income) etc. it doesn't come out that far apart.
Agreed.... depends how much you value flexibility and time to work on your own projects.
As regards finding work: agencies are essential at first, tell everyone you meet you are a freelance software guy (keep it vague: they'll probe if they need someone), friends and contacts works great but not at first, try to find a "social technology hub" in your city. These are clubs that are frequented by people who work at the big tech places and socialise, this might be a hackerspace or an exercise club. They are not always easy to find.
I am a data scientist in one of the FAANGs and feel the same. I went looking for freelance work on some websites, but they pay so little. I would be curious on how to find freelance work too.
It might take a month or two but people will start contacting you.
* this is important, your blog content/presentation actually has to be worth reading. It doesn't have to be cutting-edge, but it has to be novel enough to convince someone that you have something special to offer. Implementing a lesser-known paper and showing your results is usually a good start (also it teaches you just how hard it is to recreate something based on a paper alone).
Tangential but would you recommend just blacklisting medium and towards datascience? They always reach the top of my google searches and it drains me just reading their articles majority of the time.
If I was using one of the newer search engines that let you block domains then Medium would definitely be on my blacklist. The signal-to-noise ratio is just way too low.
towardsdatascience might be slightly better but even if you find something worthwhile, it's probably available somewhere else that doesn't clog up your search results.
Good one. Applies to software engineering practice too or any subfield of IT. On another note what resources you refer to generally to pick a paper for implementation ? Can you share some ?
Easiest way IMO is to scan the list of papers at the annual conferences in your given field, pick a handful with names that sound interesting, then try and find a paper that's referenced by two or more of them.
That's probably a good place to start - it's been around long enough that it's probably not a flash in the pan, but still "new" enough to be relevant.
Could someone explain how Data Scientists work as consulters?
I can imagine only a few cases:
* A company already has a DS team, but they are not deep enough in some domains and need help/consultation.
* The integration of the solution is simple enough and may be delivered as API.
* A company wants PoC / demo, after that they gonna hire someone to work on it.
But usually, DS needs insides into how business works and the integration of the solution may be really long-term, especially if it includes A/B tests, re-iterations over model training, datasets collection and so on. In this case, even onboarding may be long enough.
So, I'm wondering to hear about real cases that have been solved by consulters and how it generally may work.
As a consultant myself, I can say you're reasonably accurate with your assessment. Often times it works by a consulting firm beautifully presenting to non-technical execs at a company about how they can turn the company's data into money. Lots of over-confidence and some nicely "massaged" benchmark figures.
If said company hires the consulting firm, then the firm staffs the project with people who basically have to figure out how to build what was sold...typically in a short time, with a new team, none of whom understand the company's data landscape and the nuances associated with the company. You might be thinking that this is a recipe for disaster. You'd be right. It's why job satisfaction for data scientists and ML engineers in consulting is super low. But success stories do exist when the stars align and a team is able to build things like recommendation engines, supply chain optimization models, conduct a good segmentation analysis, etc. for companies that don't have sufficient talent to execute these internally.
thx fro the answer!
I always wonder how people get customers.
Do you think is better to specialize in just in one area in ML like recommendation system or try to study other ML areas (NLP, time series, etc) for freelance ?
I’ve been working on getting some data science work secured and should have a few things coming up (3-6 months, maybe sooner). Let’s chat
I work at FAANG also but as SDE and have been thinking of transitioning to a MLE role, may i ask what made you tired of your current role? just want to do something different?
Uh getting customers is the biggest trick nobody will tell you (because they just could grab them for them self) people in ML got rich by different means like scraping fiver and auto-generate content but as soon as everyone is on the wagon no one is.
I have work for you, PM me. We establish startup and need more brains and manpower.
[deleted]
Send me a direct message
farox t1_j4m771b wrote
I can't tell you about ML specifically, but maybe some useful pointers for freelancing in general. I've been in software for ~25 years, 15 or so freelancing.
First thing is that as a freelancer you're not part of "the team". This can be good or bad for you, I think it's fantastic. No dealing with political bs, I charge hourly, so no gorging with overtime etc.
But that's it. You're a tool to do a job and then leave (in theory).
In my experience most small companies won't have use for you. For one, you'll be more expensive than their employed staff, but they also want to keep that know how in house.
Mid to large companies is where you will get the most traction. However they see you as a tool. So they don't want to hire you specifically, but "an ML engineer with 6 YoE". So they outsource that problem to a recruiter or similar agency. This is for the case that you get hit by a bus, they make a phone call and get a fresh body.
So far I only had good experiences with these agencies, pay is good, it's professional and shit just gets done and you paid.
The other option is going through your network. As you have more work experience you should be able to build that and then lean on it if you have more capacity, read: looking for a job. Then you're more likely to find a smaller business because they are interested in getting you on board.
I tried my hands on those fancy new websites as well, with the same result. The problem here is also that you're more likely to compete with some kid in India that charges 1/10th of your rate.
Another thing to keep in mind: Do not go into this for the money. If you factor everything in: Vacation, sick days, hardware, licenses, pension/retirement (rule of thumb: 30% of your net income) etc. it doesn't come out that far apart.
TLDR: Computer Futures, Hays that sort of company or through your network