Sports Data Scientist · Endurance Athlete

I build the models
athletes rely on.

I implement ATL/CTL/TSB performance models from raw HR sensor data — the same mathematics behind Garmin Training Status, Strava Fitness & Freshness, and Wahoo SYSTM. Ironman finisher, ultra runner in training, PhD in precision measurement. Looking for a data role where the signal actually matters.

Targeting: Garmin·Strava·Wahoo·Zwift·TrainingPeaks
Tristan Lismer
5K
19:01
Personal best
10K
39:27
Personal best
Half Marathon
1:27:08
Personal best
Marathon
3:23:10
Personal best
Ironman 70.3
5:20:51
Personal best
Ironman
11:17:33
226 km · finisher
Ultra 80K
In training
Bromont · Oct 2026

Training Data

2026 in motion.

km run
loading…
km ridden
loading…
hours moving
run + ride combined
metres climbed
run + ride combined

Performance Management · Bromont 80K Build

Fitness · CTL
Fatigue · ATL
Form · TSB
Weeks to Bromont
Fitness · Fatigue · Form — Banister (1975) / Coggan (2003) · LTHR = 175 bpm
CTL
ATL
TSB
Weekly Running Volume
Trail Run
Road Run
Weekly Cycling Volume
Road Bike
Gravel
MTB
Weekly Elevation Gain
metres climbed
Weekly Training Stress Score
TSS
Long Run Progression ≥ 20 km — Bromont 80K Build
Distance (km)
Elevation (m)

Long Run Log ≥ 20 km

DateNameDistanceElevationPaceAvg HRTSS

Recovery & Readiness · Garmin Connect

Sleep Score — last 30 nights
Good ≥ 80
Fair 60–79
Poor < 60
HRV — Heart Rate Variability
ms
Resting Heart Rate
bpm
Training Load vs Sleep Quality — TSS → next-night sleep score
Night
Trend

Training × Recovery · Cross-Analysis

Fatigue (ATL) vs HRV — training load suppresses recovery markers
Fatigue (ATL)
HRV (ms)
Weekly Load vs Sleep — TSS and avg nightly sleep score
Weekly TSS
Avg Sleep
Daily Readiness Score — composite of sleep, HRV, resting HR
Readiness (0–100)
Run / Trail
Ride
Other
Race day

Recent Activities

Projects

Side projects.

About

Physicist by training.
Athlete by compulsion.

I'm a fourth-year PhD candidate at the Institute for Quantum Computing, Waterloo, where I build precision photonic experiments and the data pipelines that make sense of them. The work requires the same thing good sports analytics does: extracting clean signal from noisy, high-dimensional data under time pressure.

I'm an Ironman finisher (11:17:33), have run a 1:27 half marathon and a 3:23 marathon, and I'm currently deep in training for the Bromont 80K ultra in October 2026 — building CTL with every early morning run. Every activity goes into a dataset I analyse with the same tools that power Garmin and Strava.

I'm looking to bring precision science to a sport tracking company — modelling training load, processing sensor streams, and building tools that help millions of athletes make smarter decisions.

2022 – now
PhD · Quantum Optics
Institute for Quantum Computing, Waterloo
2020 – 2022
Research Intern
Canadian Nuclear Safety Commission
2018 – 2022
BScH Astrophysics
Queen's University, Kingston
Data Science
PythonNumPy / SciPyPandas Time seriesEMA filtering Signal processingCross-validationStatistical modelling
APIs & Visualisation
Strava APIGarmin Connect APIREST APIs PlotlyChart.jsmatplotlibLeaflet.js
Infrastructure
Git / GitHubGitHub Actions SLURMCompute CanadajoblibLaTeX
Physics & Optics
Precision measurementPhotonics LabVIEWMathematica

Contact

Let's build something.

Open to data roles — sport tracking & wearables

If you're building athlete-facing data products at a sport tracking company and need someone who can implement the models, process the sensor streams, and has the PRs to prove they actually use the product — reach out.

tlismer@uwaterloo.ca
Garmin
Strava
Wahoo Fitness
Zwift
TrainingPeaks / WKO
"It's the questions we can't answer that teach us the most."
— Patrick Rothfuss, The Name of the Wind
GitHub — tristanlismerphd Download CV