Data Scientist vs AI Engineer
Key Points
- Generative AI’s rapid breakthroughs have spun off a distinct discipline—AI engineering—positioning AI engineers as the emerging “sexiest job” of the 21st century.
- Data scientists act as “data storytellers,” using descriptive (EDA, clustering) and predictive (regression, classification) analytics to turn messy raw data into insights about past and future events.
- AI engineers are “AI system builders” who leverage foundation models to create generative AI solutions that reshape business processes.
- Their primary focus is on prescriptive use cases, such as decision‑optimization and recommendation‑engine design, which determine the best possible actions for an organization.
- The speaker, a former data scientist turned AI engineer at IBM, outlines four key areas where the roles differ, emphasizing the shift from insight generation to actionable AI‑driven system design.
Full Transcript
# Data Scientist vs AI Engineer **Source:** [https://www.youtube.com/watch?v=Vxw0nE1qfZc](https://www.youtube.com/watch?v=Vxw0nE1qfZc) **Duration:** 00:10:32 ## Summary - Generative AI’s rapid breakthroughs have spun off a distinct discipline—AI engineering—positioning AI engineers as the emerging “sexiest job” of the 21st century. - Data scientists act as “data storytellers,” using descriptive (EDA, clustering) and predictive (regression, classification) analytics to turn messy raw data into insights about past and future events. - AI engineers are “AI system builders” who leverage foundation models to create generative AI solutions that reshape business processes. - Their primary focus is on prescriptive use cases, such as decision‑optimization and recommendation‑engine design, which determine the best possible actions for an organization. - The speaker, a former data scientist turned AI engineer at IBM, outlines four key areas where the roles differ, emphasizing the shift from insight generation to actionable AI‑driven system design. ## Sections - [00:00:00](https://www.youtube.com/watch?v=Vxw0nE1qfZc&t=0s) **Data Scientist vs Generative AI Engineer** - The speaker explains how the rise of generative AI has birthed a distinct AI engineering role, contrasting it with traditional data scientists across four key areas of work. ## Full Transcript
for many years data science has been
called the sexiest job of the 21st
century but in recent years it seems
like there's a new job buying for that
title the AI engineer so who even are
these New Kids on the Block are they
just data scientists in Disguise what's
up y'all I'm Isaac key and I'm a former
data scientist turn AI engineer at IBM
to answer these questions I'm going to
lay out four key areas in which the work
of a data scientist differs from an AI
engineer specifically a generative AI
engineer but before before I dive into
these differences we first have to
understand more about what's happening
in the industry so traditionally data
scientists have always used AI models to
do their analysis so what's changed well
with the Advent of generative AI the
boundaries of what AI can do are being
pushed in ways that we've never seen
before and so these breakthroughs have
been so
groundbreaking that generative AI has
split off into its own distinct field
and we call that AI
engineering Okay so now that we
understand the landscape let's dive into
the differences the first area of
difference lies in the use
cases so at a very high level think of a
data scientist as a data Storyteller
they take massive amounts of messy real
world data and they use mathematical
models to translate this data into
insights on the other hand think of an
AI engineer as an AI system builder they
use Foundation models to build
generative AI systems that help to
transform business process
so since data scientists are fantastic
storytellers they use a lot of
descriptive analytics to describe the
past one example of this is through
what's called exploratory data analysis
or Eda which is all about graphing the
data and doing statistical inference
they can also do this through what's
called
clustering which group similar data
points based off of similar
characteristics such as say doing
customer segmentation
now every good story has the reader
trying to figure out what's going to
come next and that's where predictive
use cases comes in as opposed to a book
however a data scientist does not have
the end already written so they have to
use what are called machine learning
models to to make their predictions an
example of this is called regression
models which predict a numeric value
such as say a temperature or Revenue
another type of these models are
classification models which predict a
categorical value such as a success or a
failure so putting on the AI engineering
hat now one of the main use cases that
AI Engineers work on are called
prescriptive use cases which are all
about uh choosing the best course of
action an example of this is a technique
called decision
optimization which enables businesses to
assess a set of possible actions and
then choose the most optimal path based
off a set of requir requirements or
standards another example of a
prescriptive use case is through uh
creating what are called recommendation
engines uh as an example this can
involve suggesting uh targeted marketing
campaigns for a select customer
base in addition to prescriptive use
cases there are also generative use
cases hence the name generative AI now
Foundation models which why I will touch
on more in a bit enable the creation of
what are called intell
assistants uh for example a coding
assistant or a digital
adviser they also enable the creation of
chat Bots as an example which enable
conversational search through
information retrieval and the
summarization of various content so
after we have a use case identified we
need
data now people say that data is a new
oil because like oil you have to search
for and find the right data and then use
the right processes to transform it into
various products which then power
various processes for a data scientist
the oil of choice is often structured
data AKA tabular data uh do note that
data scientists still work with
unstructured data but not as much as AI
Engineers now these tables are often in
the order of hundreds to hundreds of
thousands of
observations and they require a lot of
cleaning and pre-processing before uh
the data can be modeled uh some of the
cleaning involved for example involves
uh removing outliers or joining and
filtering on a new table or even
creating new features alog together this
clean data is then used to train various
machine learning
models now on the other hand an AI
engineer for them the oil of choice is
mainly unstructured data such as text
images videos audio files Etc
uh let's take a text-based foundation
model called an llm or large language
model as an example these models require
anywhere between billions to trillions
of tokens of text to be trained on which
is a lot larger scale compared to
traditional machine learning models this
leads me to the next area of difference
which is the underlying
models so the data science toolbox
consists of hundreds of different models
and different algorithms that they can
choose
from due to the nature of these models
each different use case requires
Gathering a different data set and thus
requires training a different model and
so as a result the scope of these
individual models is a lot more narrow
meaning that it's harder for them to
generalize past the domain of data that
they've been trained on and generally
speaking these models are a lot smaller
and size in terms of the number of
parameters they take less compute power
to train and do inference and they
require less time to
train anywhere between seconds to
hours now on the other hand the
generative AI toolbox is a lot less
cluttered and it really only contains
one type of model and that is called the
foundation model now Foundation models
are revolutionary because they allow for
one single type of model to generalize
to a wide range of tasks without having
to be retrained thus their scope is
called more
wide and due to the sophistication of
these models they are a lot larger in
size often billions of
parameters they acquire require a lot
more compute power to train we're
talking hundreds to thousands of
gpus and they require a lot more
training
time now we're talking anywhere between
weeks to
months due to the differences in the
intrinsic nature between traditional
machine learning models and Foundation
models this also means that the
underlying processes and techniques that
are used to develop Solutions with these
also differ so a typical data science
process will look something like this
you start off with a use case and then
from that use case you pick the right
data then after that data is prepared
you use it to to train and validate a
model using techniques such as feature
engineering cross validation or
hyperparameter tuning as an example this
model then is
deployed at some endpoint for example in
the cloud to do real-time prediction and
inference now on the other hand the
generative AI
process also starts off with a use case
but then we can skip directly to working
with a pre-trained model
and what makes this possible is a
phenomenon called AI democratization
which is a big fancy word that simply
means making AI more widely accessible
to Everyday users some of the best
foundation models out there are
published to open source communities
such as hugging face and since these uh
models are so generalizable and so
powerful out of the box they make it
easy for developers to get started AI
Engineers interact with these Foundation
models via natural language instructions
to prompt them to do various tasks and
this process is known as prompt
engineering now prompt engineering can
be used in conjunction with different
Frameworks to then build larger AI
systems an example of these Frameworks
include uh as one chaining different
prompts together or doing what's called
parameter efficient fine-tuning or PFT
on domain specific
data or doing retrieval augmented
generation AKA rag to ground answers in
truth or even by creating autonomous
agents uh to reason through very complex
multi-step
problems so these are just a few of the
examples of the building blocks that can
be used to build larger AI
applications the last step is to then
embed the AI in a larger system or
workflow Um this can take on the form of
creating assistants or virtual agents uh
building a larger application uh with a
UI or even doing some sort of
automation so okay let's take a step
back and let's look at all the
differences at a very high level as we
can see the breakthroughs in generative
AI underpin many of the differences in
the use cases data models and processes
that data scientists and AI Engineers
work on it's important to note that
there is still overlap between the two
fields for example uh data scientists
will still work on prescriptive use
cases or an AI engineer will still work
with structured data
regardless of these differences both of
these fields are continuing to evolve at
a blazing Fast Pace with new research
papers new models new tools coming out
every single day with data Ai and a
creative mind really anything is
possible with these thank you for tuning
in I hope this was helpful until next
time
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