Research Data Scientist Spine Surgery Rsrch Group

Research Data Scientist Spine Surgery Rsrch Group (Job ID: 8857)

The main role of a Data Scientist is to implement and design scientifically rigorous approaches to examining data, apply relevant analytical techniques to different types of data (including healthcare data,) and translate the findings into meaningful, applied knowledge for researchers, collaborators, and other end users. This includes conceptualizing algorithms and study designs, creating statistical analysis plans, conducting, and interpreting analyses, and reporting findings in an applied manner.

What responsibilities will you have in this role?

Data Analyses, Statistical Modeling, and Machine Learning

• Follow detailed study protocols and analysis plans to perform a wide variety of statistical analyses, including: multivariable modeling; multilevel hierarchical analysis; longitudinal designs (including repeated measures); propensity score analysis; prediction modeling; multiple imputation.
• Carry out data analyses using statistical models and/or machine learning tasks that involve supervised learning, unsupervised learning, and/or reinforcement learning.
• Apply advanced algorithms to perform predictive analytics and develop risk scores and other derived variables.
• Apply common statistical modelling and machine learning algorithms to answer project objectives using libraries from R/Python, e.g., linear/logistic regression, survival analysis, risk adjustment, PCA, Markov Chain, k-means, GLM, GAM, GEE, SVM, random forest, neural networks, and common time series models.
• Apply models to unstructured text data, such as bag of words, LDA topic modeling, and word embeddings.
• Perform hyperparameter searches to find best fitting models.
• Compare and contrast results from different models using appropriate model performance summaries (e.g., RMSE, MAE, MAPE, F1, AUC, sensitivity, specificity, NPV, PPV, etc.).
• Prepare output of analyses for review, including graphs, tables, and interpretations of results.
• Analyze/interpret results of projects and draw conclusions for review and incorporation into reports/presentations.
• Clearly document all model details, code and iterations using appropriate commenting and version control, such as Git.
• Develop and apply advanced machine learning models, such as neural networks (feed forward, convolutional, recurrent), using a variety of libraries:(i.e., Keras, TensorFlow, PyTorch).
• Work closely with dev ops teams to put models into production.
• Help build systems to closely monitor model performance post-deployment.

Data Exploration, Preparation, and Visualization

• Ensure adequate quality control by setting standards, monitor results and institute appropriate steps for data cleaning, consistency checks and other data quality control measures prior to analysis.
• Maintain data documentation, physical and logical storage of scripts, records and master archive lists.
• Perform descriptive and inferential descriptive analysis in R and/or Python. Write HTML/PDF/Microsoft Word reports summarizing the analysis.
• Validate output tables, listings or figures generated to ensure accuracy and reliability of analyses.
• Pre-process raw data to prepare for analysis. This includes cleaning and merging data from multiple sources, as well as understanding overall data quality.
• Design and develop dashboard and reports with interactive visualizations using libraries such as Highcharts, Plotly, etc.
• Other related duties as assigned.

Qualifications:

• A Masters level degree in Clinical Epidemiology, mathematics, statistics, biostatistics, computer science, or related discipline with at least 1 year of professional data science experience.
• Ability to analyze and problem solve in the areas of data management and preprocessing, modeling, and evaluation, with consultation as needed.
• Beginner to intermediate experience (0 to 4 years) with the following: Shiny, RMarkdown, Jupyter Notebooks, HTML, CSS, Javascript, Git/GitHub/GitLab.
• Intermediate experience (4-5 years) with all of the following SQL, R and/or Python.
• Fully proficient in the use MS Office software (Word, Excel, PowerPoint, Outlook, Internet Explorer, etc.).
• Is comfortable designing and implementing basic to intermediate machine learning models using R/Python libraries under minimal supervision; linear regression, logistic regression, GLM, GAM, penalized regression, SVM, random forest, XGBoost, neural networks, and common time series models (ARIMA, holt-winter, state space models, etc.).
• Is comfortable documenting code and using version control systems such as Git under minimal consult.
• Is proficient in using common data visualization libraries from R or Python (Plotly, ggplot, matplotlib, etc.). Can build data visualizations using D3 libraries or open-source equivalents (e.g., highchart) and can prepare and automate data reports in RMarkdown or Jupyter notebooks with minimal consult.
• Must be able to read in and merge data from disparate sources, perform data quality checks, manage missing data, and prepare data for machine learning models under minimal supervision.
• Experience with unstructured data sources.
• Experience preparing data for varied statistical methods preferred.
• Experience with clinical data in a healthcare setting is a plus.
• Excellent attention to detail and proven ability to learn new skills.
• Excellent ability to learn new skills.
• Must be able to effectively communicate with end users and managers.
• Ability to analyze and problem solve in the areas of data management and preprocessing, modeling and evaluation, with consultation as needed.
• Excellent organizational skills to manage multiple tasks in a timely manner, project management skills would be an asset.
• Must be able to present to and train small groups

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Thank you for applying.