The Center for Innovations in Quality, Effectiveness and Safety
(IQuESt) is looking for data scientists to collaborate on data
science projects with utility experts. We work on cool projects
like health risk assessment, predicting adverse outcomes, signal
processing and natural language processing (NLP). The ideal
candidate will have significant experience using Python, and R, R
Shiny, and MATLAB in multiple analytical applications, such as
nonlinear time series analysis, machine learning (for example,
binomial/regression models or ensemble models), deep learning (for
example, NN, CNN, and RNN), and probability, statistics, and NLP.
As with any successful analytics project, it all starts with the
data, so you should have experience extracting and transforming
data from relational databases, i.e. SQL Server. We work with
population-level data, so experience with exceptionally large data
sets is a plus. This individual will be a full-time member of our
data science team. They will be responsible for finding creative
solutions to challenges faced by clinical investigators through an
evidence-based approach. These solutions will be found by
introducing machine learning and other data science concepts and
building applications within a scripting language, such as Python,
R, etc. This individual will engage with clinical collaborators to
fully understand their clinical questions and process flows to make
sure the solution that is provided to them will address the
The ideal candidate has an MS, or PhD in computer science, math,
engineering, stats, or data science. They should have a proven
track record of efficiently conducting and coordinating data
analytics projects both independently and collaboratively over at
least a two-year period for junior positions or a five-year period
for senior positions. Exceptional educational or industry
experience can offset any particular requirement. A background in a
diverse field that complements data science (such as applied
discipline expertise and statistics) is considered an asset.
- Translate healthcare challenges into mathematical problems
(Requirement and Scope definition).
- Develop dashboards, calculations, and reports which may require
programming in SQL or alternate programming language.
- Utilize classification modeling and decision tree models with
random forest and the accompanying boosting algorithms.
- Perform Anomaly detection modeling with isolated forests, PCA,
and K-Means clustering.
- Utilize recommendation systems and time series prediction
- Use model selection, evaluation, and interpretation concepts
like regularization, dimension reduction, and
- Develop and tune machine learning models, which may require
programming in Python and/or R.
- Prepare comprehensive documented observations, analyses and
interpretations of results including technical reports, summaries,
protocols and quantitative analyses.
- Attend regular progress meetings with clinical collaborators
(multiple times a week) face to face or via on-line meeting
software. This may include working on-site. Pending COVID-19 travel
and working restrictions.
- Bachelor's degree.
- Four years of relevant experience.
- Master's degree or PhD degree in quantitative field such as
computer science, engineering, mathematics or a related field.
- Expertise in MATLAB, R, Python or SAS.
- Expertise in SQL
- Experience participating in the research process in an academic
- Experience applying machine learning to NLP problems.
Baylor College of Medicine is an Equal Opportunity/Affirmative
Action/Equal Access Employer.