Data Science vs Machine Learning Engineer: Explained

Key Takeaways

two hot roles data scientist and machine learning engineer there are some key differences between them now which role is easiest to get into but if you want to quickly make your way into Tech roles you might want to work okay let's do this all right mic is on camera's on hey there welcome to the channel I'm Jean your trusted engineering Mentor who's going to break down the world of data science today especially two hot roles data science scientist and machine learning engineer they often get used interchangeably but there are some key differences between them so we'll start by getting a clear understanding of what data science is and what they actually do from there we'll dive into how data Engineers data scientists data analysts and machine learning Engineers all work together in a typical data science project and we'll also talk about real life examples and at the end of the video I'll tell you which of these fields might be the easiest to break into if you're just starting out in Tech so let's get into it what is data science data science combines math statistics computer science and machine learning to analyze large data sets and answer questions like what happened why it happened and what are we going to do with it now let's talk about the data science process as a whole data scientists usually team up with business stakeholders to figure out what the company needs and identify the problem next step is to get the data and this is when usually data Engineers come in data can come from all kinds of places it could be internal databases or customer relationship management software like CRM web server logs or even social media so data Engineers usually use tools like SQL and big query to together and organize the data they also design create and maintain the systems that manage the data which usually involves a lot of coding creating data models developing data Pipeline and what's called ETL managing the extract transform and load process then data scientists will usually explore the data using statistics visualization and other techniques to understand what is happening here this helps them find patterns and figure out what might be worth investigating further next step is to model the data to predict outcomes or suggest strategies this is where you use machine learning techniques like Association classification clustering to train the data set then finally you turn the findings into something useful for the business and this is usually when data analysts come in to create charts graphs and other visuals to make the results easy to understand now you might be wondering if data scientists use machine learning how is it different from a machine learning engineer the machine learning Engineers focus on algorithms patterns and building models and in a typical data science project data scientists might use machine learning techniques themselves or collaborate with machine learning engineers and the caveat is there's a big difference between big tech companies and startups in large companies uh data scientists typically work in teams with analysts data Engineers machine learning engineer and they all collaborate together in the whole data science process with the end goal of helping the business achieve its goals whatever it may be but in smaller companies data scientists may have to do a little bit of everything one person might work on data engineer ing analysis and even machine learning and it could even be some software engineer doing a little bit of data science and this is similar to how at large tech companies roles like software engineer product manager project manager Tech lead engineering managers are all clearly separated with each person focusing on very specific set of responsibilities but in a startup one person might wear multiple hats doing a little bit of everything from coding to project management and product management now let's look at some data science examples since you're watching Youtube We can think about personalization and recommendation system think about how YouTube or Netflix seems to know exactly what you want to watch next they use machine learning algorithms to analyze your watch history and identify patterns by doing this they can recommend movies or videos that match your preferences their goal is to create a personalized experience to keep you watching now let's look at some real life drop postings at Netflix I found the role for data scientist L5 for ads and a machine learning engineer L5 for llm application I'll leave the links to the job descriptions below so you can take a look at it too now let's break down the key differences between the two roles at Netflix in terms of the technical deliverables the data scientist role mentions data exploration deliver well documented data sets and reports compute and validate appropriate metrics meanwhile machine learning engineer role says experience with llm Ops tooling llm agent apis and llm lots of llm it also says cloud computing providers such as AWS optimize llm serving Library such as deep speed tensor RT now I'm thirsty okay now let's look at the degree requirements this data scientist posting does not specify the degree requirements here and it could be because a degree in data science is pretty new most people don't actually study data science but they come from a lot of different kinds of majors and transfer into data science whereas for the machine learning engineering role it does specifically say that you're required to have a bachelor's slms in computer science and I made another video explaining the statistics about the degree that people hold when they're applying for roles like machine learning so you should go check that out and I often get comments from people asking like what are the skills needed to like get into these rules and my advice for you is to instead of asking me go look at the job listings out there you can search for them on LinkedIn and there's so many job tools out there you can just look it up for free like look up 5 to 10 job descriptions from various companies that you might be interested in applying for because that is really the best way to get a clear picture of what skills and experiences companies are really looking for okay the final topic now which role is easiest to get into well if you ask me I would say data analyst is probably your best bed and it's because it's a little bit more straightforward with less complex requirements and not as much coding involved whereas if you're applying for machine learning roles you would often be competing with PhD holders and even for data scientist roles to requirements are a lot more complex and obviously if you can land a job as a machine learning engineer right off the bat go for it but machine learning is just a much tougher field to break into you need to know all the complex algorithms Advanced coding and deep understanding of machine learning Concepts which is even a mouthful to just even say and it just takes so much longer to learn plus companies don't often even hire Junior people for these roles so you really need to come with some solid experience under your belt and I guess you could also spend 10 years in school like four years in under couple years in master and few more in PhD but if you want to quickly make your way into Tech roles you might want to work your way up to machine learning by gaining experiences in other data roles like data analyst first and then build your way up to it in fact I just interviewed a data scientist because initially out of school she couldn't directly get a job as a data scientist she got a job as a business analyst and then she eventually worked her way into data science I am so working on that video so that will be coming next so subscribe if you want to get notified for that video I do want to show you a data analyst role also at Netflix so the tech requirements for this job posting are a SQL and python or similar languages Big Data Technologies like Hadoop and Spark and visualization tool like Tao now this particular role is a senior role but if you do some searching you can find a lot of different options for data scien and data analyst and other analyst type of roles now if you're wondering about the top data scientist salaries watch this video otherwise YouTube thinks you should watch this one next I'll see you there bye