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Data Science Personal Statement
Leo
Chen
leo
.
chen
@
university
.
edu
• (510) 555-0182 •
San
Francisco
,
CA
•
July
15, 2026
My
first
encounter
with
data
science
was
not
in
a
classroom
,
but
in
a
basement
lab
during
my
sophomore
year
.
I
was
trying
to
predict
equipment
failures
for
the
university
robotics
club
using
sensor
logs
.
That
weekend
,
buried
in
Python
notebooks
and
scatter
plots
,
I
realized
that
patterns
hidden
in
noise
could
tell
stories
and
solve
real
problems
.
Since
then
,
I
have
pursued
every
opportunity
to
turn
raw
data
into
actionable
insight
,
and
I
am
now
eager
to
formalize
that
passion
through
graduate
study
.
I
graduated
Summa
Cum
Laude
from
the
University
of
Washington
in
June
2025
with
a
Bachelor
of
Science
in
Applied
Mathematics
and
Computer
Science
,
maintaining
a
3.92
GPA
.
My
coursework
built
a
rigorous
foundation
in
probability
,
linear
algebra
,
and
optimization
,
while
upper
-
division
classes
such
as
Statistical
Machine
Learning
,
Bayesian
Inference
,
and
Distributed
Systems
pushed
me
to
implement
theory
at
scale
.
In
Professor
Elena
Voss
'
s
computational
statistics
seminar
,
I
co
-
authored
a
paper
on
sparse
Gaussian
process
approximations
that
was
accepted
to
the
university
'
s
undergraduate
research
journal
.
That
experience
taught
me
that
elegant
mathematics
must
meet
robust
engineering
to
produce
reliable
science
.
During
my
junior
year
,
I
joined
the
UW
Sensor
Systems
Lab
,
where
I
spent
eighteen
months
building
predictive
maintenance
pipelines
for
autonomous
ground
vehicles
.
Working
with
a
dataset
of
14,000
multivariate
time
-
series
sequences
,
I
trained
LSTM
and
Transformer
-
based
models
to
forecast
actuator
degradation
.
By
engineering
spectral
features
and
deploying
an
early
stopping
regime
based
on
validation
loss
curvature
,
I
improved
the
baseline
mean
time
to
failure
prediction
by
34%.
The
project
demanded
fluency
in
PyTorch
,
Pandas
,
and
DVC
for
experiment
tracking
,
and
it
cemented
my
belief
that
careful
data
curation
matters
more
than
model
complexity
.
In
parallel
,
I
collaborated
with
the
School
of
Public
Health
on
an
NLP
project
analyzing
2.3
million
tweets
related
to
vaccine
hesitancy
.
I
fine
-
tuned
a
distilled
BERT
model
for
sentiment
classification
and
applied
latent
Dirichlet
allocation
to
uncover
shifting
discourse
topics
.
Our
findings
,
which
revealed
a
strong
correlation
between
geographic
misinformation
clusters
and
subsequent
regional
vaccination
rates
,
were
presented
at
the
2025
Pacific
Northwest
Data
Science
Summit
.
This
work
showed
me
how
data
science
can
illuminate
social
dynamics
and
inform
public
policy
.
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Most
recently
,
I
interned
at
Meridian
Analytics
,
a
Seattle
-
based
fintech
startup
,
where
I
built
an
explainable
pricing
model
for
a
small
-
business
lending
product
.
Using
gradient
-
boosted
trees
and
SHAP
values
,
I
created
a
framework
that
allowed
underwriters
to
understand
feature
contributions
for
every
loan
decision
.
The
model
reduced
default
risk
by
12%
while
cutting
average
approval
time
from
four
days
to
eight
hours
.
Working
alongside
product
managers
and
compliance
officers
taught
me
that
the
best
models
are
those
that
earn
the
trust
of
their
end
users
.
I
am
applying
to
the
M
.
S
.
in
Data
Science
program
at
Stanford
University
because
of
its
deep
commitment
to
interdisciplinary
research
and
ethical
computing
.
I
am
particularly
drawn
to
Professor
David
Park
'
s
work
on
causal
inference
in
healthcare
and
the
HAI
Center
'
s
focus
on
human
-
centered
artificial
intelligence
.
My
long
-
term
goal
is
to
build
decision
-
support
systems
in
clinical
environments
where
model
interpretability
is
not
a
luxury
,
but
a
safety
requirement
.
I
want
to
push
the
boundary
of
what
machine
learning
can
explain
while
ensuring
that
vulnerable
populations
are
not
harmed
by
opaque
automation
.
Data
science
is
not
just
a
career
path
for
me
;
it
is
a
way
of
asking
better
questions
about
the
world
.
My
background
in
mathematics
,
my
hands
-
on
research
in
time
-
series
and
NLP
,
and
my
industry
experience
in
model
explainability
have
prepared
me
to
contribute
meaningfully
to
your
program
.
I
am
excited
to
bring
my
curiosity
,
my
technical
skills
,
and
my
commitment
to
responsible
AI
to
the
Stanford
community
.
Thank
you
for
considering
my
application
.
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