When I first learned data science (5+ years ago), data engineering and ML engineering were not as widespread as they are today. Consequently, the role of a data scientist was often more broadly defined than what we may see these days.
For example, data scientists may have written ETL scripts, set up databases, performed feature engineering, trained ML models, and deployed models into production.
Although it is becoming more common to split these tasks across multiple roles (e.g., data engineers, data scientists, and ML engineers), many situations still call for contributors who are well-versed in all aspects of ML model development. I call these contributors full-stack data scientists.
More specifically, I see a full-stack data scientist as someone who can manage and implement an ML solution end-to-end. This involves formulating business problems, designing ML solutions, sourcing and preparing data for development, training ML models, and deploying models so their value can be realized.
Given the rise of specialized roles for implementing ML projects, this notion of FSDS may seem outdated. At least, that was what I thought in my first corporate data science role.
These days, however, the value of learning the full tech stack is becoming increasingly obvious to me. This all started last year when I interviewed top data science freelancers from Upwork.
Almost everyone I spoke to fit the full stack data scientist definition given above. This wasn’t just out of fun and curiosity but from necessity.
A key takeaway from these interviews was data science skills (alone) are limited in their potential business impact. To generate real-world value (that a client will pay for), building solutions end-to-end is a must.
But this isn’t restricted to freelancing. Here are a few other contexts where FSDS can be beneficial
- An SMB (small-medium business) with only 1 dedicated resource for AI/ML projects
- A lone AI/ML contributor is embedded in a business team
- Founder who wants to build an ML product
- Individual contributor at a large enterprise who can explore projects outside established teams
In other words, full-stack data scientists are generalists who can see the big picture and dive into specific aspects of a project as needed. This makes them a valuable resource for any business looking to generate value via AI and machine learning.
While FSDS requires several skills, the role can be broken down into four key hats: Project Manager, Data Engineer, Data Scientist, and ML Engineer.
Of course, no one can be world-class in all hats (probably). But one can certainly be above average across the board (it just takes time).
Here, I’ll break down each of these hats based on my experience as a data science consultant and interviews with 27 data/ML professionals.
The key role of a project manager (IMO) is to answer 3 questions: what, why, and how. In other words, what are we building? Why are we building it? How will we do it?
While it might be easy to skip over this work (and start coding), failing to put on the PM hat properly risks spending a lot of time (and money) solving the wrong problem. Or solving the right problem in an unnecessarily complex and expensive way.
The starting point for this is defining the business problem. In most contexts, the full-stack data scientist isn’t solving their problem, so this requires the ability to work with stakeholders to uncover the problem’s root causes. I discussed some tips on this in a previous article.
Once the problem is clearly defined, one can identify how AI can solve it. This sets the target from which to work backward to estimate project costs, timelines, and requirements.
Key skills
- Communication and managing relationships
- Diagnose problems and design solutions
- Estimating project timelines, costs, and requirements
In the context of FSDS, data engineering is concerned with making data readily available for model development or inference (or both).
Since this is inherently product-focused, the DE hat may be more limited than a typical data engineering role. More specifically, this likely won’t require optimizing data architectures for several business use cases.
Instead, the focus will be on building data pipelines. This involves designing and implementing ETL (or ELT) processes for specific use cases.
ETL stands for extract, transform, and load. It involves extracting data from their raw sources, transforming it into a meaningful form (e.g., data cleaning, deduplication, exception handling, feature engineering), and loading it into a database (e.g., data modeling and database design).
Another important area here is data monitoring. While the details of this will depend on the specific use case, the ultimate goal is to give ongoing visibility to data pipelines via alerting systems, dashboards, or the like.
Key skills
- Python, SQL, CLI (e.g. bash)
- Data pipelines, ETL/ELT (Airflow, Docker)
- A cloud platform (AWS, GCP, or Azure)
I define a data scientist as someone who uses data to uncover regularities in the world that can be used to drive impact. In practice, this often boils down to training a machine learning model (because computers are much better than humans at finding regularities in data).
For most projects, one must switch between this Hat and Hats 1 and 2. During model development, it is common to encounter insights that require revisiting the data preparation or project scoping.
For example, one might discover that an exception was not properly handled for a particular field or that the extracted fields do not have the predictive power that was assumed at the project’s outset.
An essential part of model training is model validation. This consists of defining performance metrics that can be used to evaluate models. Bonus points if this metric can be directly translated into a business performance metric.
With a performance metric, one can programmatically experiment with and evaluate several model configurations by adjusting, for example, train-test splits, hyperparameters, predictor choice, and ML approach. If no model training is required, one may still want to compare the performance of multiple pre-trained models.
Key Skills
- Python (pandas/polars, sklearn, TensorFlow/PyTorch)
- Exploratory Data Analysis (EDA)
- Model Development (feature engineering, experiment tracking, hyperparameter tuning)
The final hat involves taking the ML model and turning it into an ML solution—that is, integrating the model into business workflows so its value can be realized.
A simple way to do this is to containerize the model and set up an API so external systems can make inference calls. For example, the API could be connected to an internal website that allows business users to run a calculation.
Some use cases, however, may not be so simple and require more sophisticated solutions. This is where an orchestration tool can help define complex workflows. For example, if the model requires monthly updates as new data become available, the whole model development process, from ETL to training to deployment, may need to be automated.
Another important area of consideration is model monitoring. Like data monitoring, this involves tracking model predictions and performance over time and making them visible through automated alerts or other means.
While many of these processes can run on local machines, deploying these solutions using a cloud platform is common practice. Every ML engineer (MLE) I have interviewed uses at least 1 cloud platform and recommended cloud deployments as a core skill of MLEs.
Key Skills
- Containerize scripts (Docker), build APIs (FastAPI)
- Orchestration — connecting data and ML pipelines (AirFlow)
- A cloud platform (AWS, GCP, or Azure)
While a full-stack data scientist may seem like a technical unicorn, the point (IMO) isn’t to become a guru of all aspects of the tech stack. Rather, it is to learn enough to be dangerous.
In other words, it’s not about mastering everything but being able to learn anything you need to get the job done. From this perspective, I surmise that most data scientists will become “full stack” given enough time.
Toward this end, here are 3 principles I am using to accelerate my personal FSDS development.
- Have a reason to learn new skills — e.g. build end-to-end projects
- Just learn enough to be dangerous
- Keep things as simple as possible — i.e. don’t overengineer solutions
A full-stack data scientist can manage and implement an ML solution end-to-end. While this may seem like overkill for contexts where specialized roles exist for key stages of model development, this generalist skillset is still valuable in many situations.
As part of my journey toward becoming a full-stack data scientist, future articles of this series will walk through each of the 4 FSDS Hats via the end-to-end implementation of a real-world ML project.
In the spirit of learning, if you feel anything is missing here, I invite you to drop a comment (they are appreciated) 😁
