IT Professional

University of Colorado•Aurora, CO

1d•$29 - $40•Remote

About The Position

Alzheimer’s disease is a leading cause of death in the United States, and scalable digital tools for prevention and early detection are urgently needed. Our team is building machine learning–driven digital biomarkers from wearable sleep electroencephalography (EEG) to assess neurodegenerative changes before symptoms appear. Some examples of projects include the following: Brain aging phenotypes derived from EEG-based digital features Cognitive decline trajectories linking sleep-related physiology to performance Multimodal correlates, integrating EEG with neuroimaging and Alzheimer’s molecular/structural measures We’re seeking a Part-Time Data Scientist who can analyze real‑world datasets and build well‑documented, reproducible applied mathematical analysis pipelines in Python. You will drive exploratory data analysis, feature engineering, and model development, while applying solid documentation and coding practices that make results reliable and repeatable. Candidates with backgrounds in Applied Mathematics who bring strong data‑analysis skills and practical ML experience are encouraged to apply. Equivalent real‑world experience is welcome.

Requirements

Bachelor’s in Computer Science, Electrical/Computer Engineering, Data Science, Statistics, Applied Math, Neuroscience, or a related field from an accredited institution.
One (1) year of professional IT project management experience
Substitution: An advanced degree (Masters or Doctorate) may be substituted for experience on a year for year basis if the degree is in a field of study directly related to the work assignment.

Nice To Haves

Master’s degree in a related field
Applied math methods relevant to biosignals: coursework or project experience in time-series analysis, statistical learning, signal processing, numerical optimization, inverse problems, or stochastic processes—especially applied to noisy real-world data.
Biosignal/EEG experience: exposure to EEG (sleep or otherwise) or related physiological signals (ECG, actigraphy, respiration); familiarity with common preprocessing/feature concepts (filtering, spectral features, artifact handling). Experience with tools such as MNE-Python, YASA, or comparable MATLAB/R toolboxes is a plus.
Practical model evaluation skills: experience designing train/test splits and cross-validation for subject-level/time-series data; structured error analysis; calibration/thresholding concepts; ability to compare baseline statistical models and communicate tradeoffs.
Research tooling & workflow maturity: experience with reproducible workflows (clean repo structure, scripted runs, configuration files), basic environment management, and/or experiment logging.
Human-subjects / regulated data awareness: experience working with IRB-governed datasets, data use agreements, or privacy constraints (HIPAA-aligned handling where applicable).
interest in sleep neurophysiology, brain aging, and neurodegeneration; enthusiasm for building practical, scalable digital biomarkers from wearable data.
Ability to communicate effectively, both in writing and orally and follow through
Ability to establish and maintain effective working relationships with employees at all levels throughout the institution.
Outstanding customer service skills.
Applied mathematics foundation: strong grounding in linear algebra, probability, statistics, and numerical methods; ability to translate scientific questions into quantitative objectives and assumptions.
Quantitative modeling & inference: comfort with regression/classification fundamentals, regularization concepts, model diagnostics, and uncertainty/variability; ability to reason about overfitting and generalization even when using simple baselines.
Time-series / signal analysis mindset: familiarity with core concepts such as sampling, filtering, stationarity/nonstationarity, spectral methods (e.g., FFT/PSD concepts), feature extraction, and artifact/noise considerations in real-world signals.
Programming for scientific computing (ramp-up supported): working proficiency in at least one scientific computing environment (Python, MATLAB, or R) and the ability to implement and test analysis routines; comfort learning and extending an existing Python codebase (NumPy/pandas-style workflows, basic scripting, Jupyter).
Reproducible analysis habits: produces clear, well-organized analyses with documented assumptions, versioned outputs, and readable code; willing to follow team standards for structure, naming, and reviewability.
Data handling & quality awareness: ability to clean, join/reshape, and validate multi-source datasets; recognizes common data issues (missingness, label alignment, outliers, leakage risks) and can implement basic checks.
Communication & collaboration: can explain quantitative results to mixed audiences (scientists/clinicians/engineers), create clear figures/tables, and write concise summaries of methods, results, limitations, and next steps.

Responsibilities

Perform exploratory data analysis on sleep EEG and related datasets; define targets, features, and baselines aligned with digital biomarker goals.
Develop, evaluate, and compare models for classification/regression using sound validation design, including cross-validation, regularization, calibration, and structured error analysis.
Translate scientific questions into measurable model objectives and evaluation criteria, balancing performance with robustness and interpretability.
Document experiments and results clearly; draft brief model cards and dataset summaries, including assumptions, limitations, and intended use.
Build and maintain robust workflows to ingest, validate, and transform sleep EEG and related signals into analysis-ready formats.
Implement preprocessing and feature-generation steps appropriate for wearable biosignals, with attention to data quality, artifact handling, and consistent labeling/alignment.
Implement data quality checks and lightweight reporting to catch issues early (e.g., missingness, signal quality variation, label inconsistencies).
Other Duties assigned