Machine Learning Engineer vs. Data Scientist: Career Comparison

The
transition
to
a
digital
landscape
is
tough
for
businesses
struggling
to
use
their
data
to
achieve
a
competitive
advantage.
Businesses
need
data-driven
strategies
to
tap
into
the
power
of
data,
thereby
increasing
the
demand
for
data
experts.
The
machine
learning
engineer
vs.
data
scientist
debate
is
an
outcome
of
the
growing
demand
for
data
in
businesses.

Professionals
who
can
help
businesses
make
the
most
of
their
data
are
likely
to
enjoy
promising
career
benefits.
However,
the
similarities
between
the
jobs
of

machine
learning
engineers
and
data
scientists
create
misconceptions
about
the
roles. 

On
the
other
hand,
the
two
roles
are
associated
with
different
responsibilities
and
demand
different
skillsets.
For
example,
machine
learning
engineers
have
the
special
skills
to
deploy

machine
learning
models.
Data
scientists
have
a
broad
range
of
skills
related
to
data
collection
and
analytics.
Let
us
explore
the
notable
differences
between
an
ML
engineer
and
a
data
scientist
to
identify
the
best
career
path. 

Why
Should
You
Compare
an
ML
Engineer
with
a
Data
Scientist?

The
best
answer
to
such
questions
would
point
to
the
changing
dynamics
of
the
labor
market
for
data-centric
job
roles.
The
growing
adoption
of
AI
systems
has
led
to
the
expansion
of
the
global

machine-learning
market,
which
might
grow
beyond
$170
billion
by
2030. 

It
is
also
important
to
note
that
the
debate
on
data
scientists
vs.
machine
learning
engineers
revolves
around
the
significance
of
data
scientists.
The
US
Bureau
of
Labor
Statistics
has
predicted
that
the
demand
for
data
scientists
will
grow
by
35%
from
2022
to
2032.

You
can
notice
that
both
career
paths
are
equally
promising
and
can
help
you
enjoy
the
best
rewards
of
professional
development.
Therefore,
a
detailed
comparison
of
the
responsibilities,
expected
salary
estimate,
skills,
and
technology
required
for
the
two
roles
is
an
essential
priority
for
many
professionals.   

What
are
the
Differences
in
the
Career
of
ML
Engineers
and
Data
Scientists?

The
decision
to
pursue
a
career
as
a
machine
learning
engineer
or
a
data
scientist
is
a
difficult
one,
considering
the
strong
demand
for
data
experts.
On
top
of
it,
some
people
ignore
the
data
scientist
and
machine
learning
engineer
difference
due
to
blurred
lines
between
the
roles.
For
example,
general
assumptions
about
data
scientists
may
suggest
that
they
only
work
with
data
analytics,
and
ML
engineers
can
only
build
models.
On
the
contrary,
you
would
find
a
lot
of
differences
between
the
two
roles
and
discover
that
they
require
a
special
set
of
skills.
Here
is
an
overview
of
the
differences
between
a
career
as
a
machine
learning
engineer
and
a
data
scientist.

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What
are
the
Responsibilities
of
ML
Engineers
and
Data
Scientists?

The
foremost
point
for
comparison
between
the
roles
of
machine
learning
engineers
and
data
scientists
is
the
outline
of
responsibilities.
What
would
you
have
to
do
every
day
on
the
job
as
a
machine
learning
engineer
or
a
data
scientist? 

Interestingly,
machine
learning
engineers
and
data
scientists
have
common
responsibilities
and
fundamental
skills.
For
example,
machine
learning
engineers
and
data
scientists
must
have
a
strong
command
over
programming
languages
like
Python
for
effective
data
analysis
and
manipulation.
On
top
of
it,
ML
engineers
and
data
scientists
must
have
an
in-depth
understanding
of
important
machine
learning
frameworks
required
for
the
efficient
development
and
deployment
of
ML
models. 

• 
  Responsibilities
  of
  Data
  Scientists 

Data
scientists
are
responsible
for
extracting
insights
from
data
to
help
in
driving
business
decisions.
The
job
responsibilities
of
data
scientists
can
provide
the
right
explanation
for
queries
like
“Who
earns
more,
data
scientist
or
machine
learning
engineer?”
by
emphasizing
the
special
skills
required
to
address
them.

The
responsibilities
of
data
scientists
include
data
collection
and
cleaning,
algorithm
selection,
experimentation,
and
custom
model
development.
Data
scientists
collect
data
from
different
sources
and
refine
it
through
preprocessing
techniques
alongside
achieving
data
integrity
and
quality.
Data
scientists
also
identify
the
right
data
patterns
to
identify
the
best
data
structures
and
ML
algorithms.
In
some
cases,
data
scientists
also
design
machine
learning
models
according
to
unique
business
challenges.

• 
  Responsibilities
  of
  Machine
  Learning
  Engineers

The
most
crucial
responsibility
of
machine
learning
engineers
revolves
around
deploying

artificial
intelligence
models
to
production
at
scale.
The
salary
comparison
between
an
ML
engineer
and
a
data
scientist
should
also
draw
the
limelight
on
the
responsibilities
of
machine
learning
engineers.
ML
engineers
have
to
implement
processes
such
as
rigorous
testing,
model
deployment,
and
model
optimization.

In
addition,
machine
learning
engineers
must
also
ensure
that
ML
systems
can
manage
the
growing
volume
of
user
interactions
and
data.
ML
engineers
must
also
implement
rigorous
and
frequent
testing
of
models
to
ensure
better
accuracy
in
real-world
applications.
On
top
of
that,
machine
learning
engineers
must
optimize
the
models
to
ensure
better
user
experiences. 

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What
are
the
Skills
Required
for
ML
Engineers
and
Data
Scientists?

The
next
crucial
point
of
comparison
between
a
machine
learning
engineer
and
a
data
scientist
is
the
outline
of
skills
required
for
the
roles.
The
machine
learning
engineer
vs.
data
scientist
debate
draws
many
similarities
between
the
skills
required
for
the
jobs.
However,
you
would
find
subtle
differences
between
the
skills
required
for
ML
engineer
and
data
scientist
jobs.

Data
scientists
need
skills
such
as
problem-solving,
statistics,
data
science,
and
data
analytics
and
data
visualization
techniques.
They
share
a
similar
skill
requirement
with
machine
learning
engineers,
programming.
In
addition,
they
must
also
learn
about
the
fundamentals
of
machine
learning
techniques
such
as
supervised
and
unsupervised
learning.
Data
scientists
also
need
presentation
skills
and
written
and
verbal
communication
skills.

On
the
other
hand,
the
data
scientist
vs.
machine
learning
engineer
comparison
must
draw
your
attention
to
the
specialized
skills
of
ML
engineers.
Machine
learning
engineers
must
know
about
data
structures,
software
engineering,
data
modeling,
and
statistics.
On
top
of
it,
ML
engineers
must
have
in-depth
conceptual
knowledge
of
machine
learning
and
different
popular
machine
learning
frameworks.

What
is
Tech
Stack
Used
by
ML
Engineers
and
Data
Scientists?

Another
important
point
for
comparing
the
careers
of
ML
engineers
and
data
scientists
revolves
around
the
tech
stack
used
in
both
roles.
The
tech
stack
is
an
important
factor
in
determining
answers
to
“Who
earns
more
data
scientist
or
machine
learning
engineer?”
due
to
the
specialization
required
for
using
the
technologies.
Interestingly,
you
would
find
many
common
additions
in
the
tech
stack
for
ML
engineers
and
data
scientists.
For
example,
machine
learning
engineers
and
data
scientists
must
be
fluent
in
Python,
Git,
AWS,
and
Azure.
They
must
also
know
about
algorithms
and
neural
networks
and
have
the
skills
to
develop
advanced
ML
models.

Data
scientists
would
need
special
skills
in
programming
languages
like
R
and
SQL
for
data
mining,
statistical
modeling,
and
database
management.
Data
scientists
must
also
know
Microsoft
Excel
and
its
uses
for
basic
analytics,
data
refining
tasks,
and
initial
data
exploration. 

Matplotlib
is
also
an
important
addition
to
the
tech
stack
of
data
scientists
as
it
helps
with
data
visualization.
Data
scientists
also
use
Jupyter
notebooks
for
interactive
data
analysis
and
collaboration.
Most
importantly,
data
scientists
also
leverage
Python
libraries
such
as
Scikit-learn
to
create
and
deploy
ML
models. 

The
difference
between
data
science
and
machine
learning
engineering
also
draws
attention
to
the
specialized
tools
included
in
the
tech
stack
for
ML
engineers.
For
example,
ML
engineers
must
know
C++
to
optimize
and
implement
high-performance
ML
algorithms.
Machine
learning
engineers
must
be
fluent
in
using
Linux
OS,
the
most
popular
operating
system
for
ML
workloads.

ML
engineers
should
also
know
the
best
practices
for
using

deep
learning
frameworks
such
as

TensorFlow
and
PyTorch.
In
addition,
ML
engineers
must
also
have
practical
expertise
in
frameworks
such
as
TorchServe
that
help
in
the
efficient
deployment
and
management
of
ML
models. 

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What
is
the
Salary
of
ML
Engineer
and
Data
Scientist?

The
most
important
aspect
in
the
comparison
between
career
of
ML
engineers
and
data
scientists
is
the
salary.
ML
engineers
and
data
scientists
have
a
specialized
skill
set
thereby
boosting
their
expected
salary
estimates.
The
ML
engineer
vs.
data
scientist
salary
comparison
is
important
as
both
the
roles
have
a
strong
demand
and
offer
higher
earning
potential
than
other
jobs.
Apart
from
the
salary,
machine
learning
engineers
and
data
scientists
enjoy
many
other
benefits
from
their
professional
growth.

The
average
annual
salary
for
an
entry-level
data
scientist
is
$103,500. 

The
average
annual
salary
of
an
ML
engineer
is
almost
$136,600. 

It
is
important
to
remember
that
the
salary
of
an
ML
engineer
or
a
data
scientist
would
vary
according
to
experience,
industry
and
location.
In
addition,
valuable
skills
and
business
acumen
can
push
a
professional
towards
new
heights
of
success
in
both
the
roles.

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Final
Words 

The
comparison
between
the
careers
of

machine
learning
engineers
and
data
scientists
reveals
that
both
of
them
are
in-demand
roles.
As
the
machine
learning
engineer
vs.
data
scientist
debate
gains
momentum,
it
is
important
to
identify
the
most
relevant
points
of
comparison.
Some
of
the
notable
aspects
for
comparison
include
responsibilities,
skill
requirements,
tech
stack,
and
salary
for
the
roles.
The
comparison
between
the
two
roles
can
help
an
aspiring
professional
find
the
right
career
path
aligned
with
their
career
goals
and
existing
skills.
Find
the
best
training
resources
for
ML
engineer
and
data
scientist
roles
for
a
highly
rewarding
career
now.