Qualifications

Master’s degree in Computer Science, Mathematics, Statistics, Engineering, or other computational/quantitative field is required. PhD is preferred.
2 or more years of advanced data analysis experience and expertise in diverse statistical, data mining techniques and technologies including:
Neural networks, deep learning, Naïve Bayes, regression, random forest, clustering, text mining, social network analysis
Natural Language Processing (NLP)
Supervised and unsupervised machine learning
Model validation, testing, and communication
Machine Learning frameworks like scikit-learn, Tensorflow, Keras, pandas, etc.
Experience working with Microsoft Azure cloud based technologies is preferred
Proficiency with a statistical language: R or Python is required and analytical documentation: Jupyter or iPython notebook is preferred
Strong programming skills: shell scripting, Python, Perl, C++, SQL and Java are preferred
Experience working with Tableau, Power BI, matplotlib, ggplot2 or similar data visualization tool is required
Experience with healthcare data and/or EHR data is preferred
Demonstrated experience synthesizing and analyzing large data and making program recommendations based on that data is required
Excellent written and communication skills explaining complex quantitative models to business stakeholders, management and executives
Experience performing statistical analysis to quantify the limitations of models
Ability to take the inferences from data and produce decisions from it that will optimize objectives
Ability to transfer knowledge and concepts to the team that is implementing the actual system
Ability to grasp data and see patterns or make inferences at a high level
Strong problem-solving and metadata skills
Strong staff development, leadership and coaching skills
Strong organizational and interpersonal skills will be needed in our collaborative and fast-paced team
Demonstrated ability to influence and shape consensus, lead group discussions and presentations to clinical and business operations leaders from across the organization
Ability to establish and maintain a spirit of cooperation and respect, flourish in an unstructured environment, and always engage in professional and ethical conduct
Flexibility with ad-hoc investigations and changes in requirements
Must recognize problems, evaluate, and refer to the appropriate channels for action
High functioning team skills, including active listening, rapport building, receiving and delegating work assignments
Ability to probe for information about the underlying needs of the organization
Ability to identify, document, resolve and/or escalate issues and maintain a complete issues log
Ability to manage multiple and competing tasks
Experience with Machine Learning Operations (MLOPs) or similar principles. Proven record of operational support for Machine Learning models
Has strong interests in learning Responsible AI concepts and participate in development of custom Responsible AI assets for UCLA

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