CHOC Children's
Research Computational Data Scientist II - Grant-funded
CHOC Children's, Orange, California, United States, 92613
Work LocationOrange, CaliforniaWork ShiftDay – 08hrs (United States of America)Why CHOC?At CHOC, we strive to be the leading destination of children’s health by providing exceptional and innovative care. We are responsible for the overall health of our community’s pediatric population in our hospitals, clinics, and practices. And because of our breadth of care, your career at CHOC can be as diverse and fulfilling as you determine. CHOC’s compensation structure, benefits offerings, and career development programs are geared to helping you achieve your professional and personal goals. Apply now to see where your career at CHOC can take you.Job SummaryThe Research Computational Scientists (RCS) II will collaborate with providers, researchers, and other associates to provide computational services required for research and implementation of research findings. This requires a multidisciplinary training in the computational and data sciences including statistics, computer science, and artificial intelligence. The role will support the development and innovative application of advanced simulation, data analyses, visual analytics, or other computational techniques to research problems across the CHOC Health System. Daily tasks include working closely with providers, researchers, and other associates to develop or deploy computational solutions required for improving knowledge, the health of patients, and the financial health of the organization. These computational solutions will include development of new statistical, machine-learning, and artificial intelligence algorithms, improvement of existing algorithms, and analyses of data from the electronic medical records of patients. Data sources will span not just the electronic medical records but other sources of data relevant to research and organizational growth. The goal is the development of deeper understanding, insight, and prediction of events that may foster the health of children and the health of the care system as well.Pay RangeMinimum $95,950 Midpoint $127,150 Maximum $158,350Rate of pay is determined by various factors, including but not limited to knowledge, skills, competencies, experience, education, as well as position requirements.ExperienceRequired:
Minimum five years of experience with a Master of Science degree, or entry level experience with a Doctorate degree. Experience in the application of advanced statistical algorithms and computational and data science methods in the analyses of medical data including generalized linear models across a wide range of statistical distributions, time series analyses, and longitudinal data analyses. Familiarity with Machine-learning, Artificial Intelligence, Casual Inference or Bayesian Statistics is also required through prior coursework, publications, or informal studies of these methods. Previous experience with high-level programming languages such as SAS, R Statistical Programming Language, SPSS, C/C++, Python, and TensorFlow, Keras or PyTorch for time series and other sequence data prediction. Experience with SQL programming and data preprocessing for research studies are also required. Previous participation in research during academic training, internships, or prior job experience that resulted in one or more peer-reviewed publications.Preferred:
Prior peer-reviewed publications in PubMed-indexed journals include first or last authorship.EducationRequired:
Master of Science (with 5 years of experience) or entry-level Doctorate in the computational and data sciences including statistics, artificial intelligence, biostatistics, bioinformatics, and statistical genetics.Preferred:
Doctorate in the computational and data sciences, artificial intelligence, statistics, biostatistics, bioinformatics, and statistical genetics.TrainingRequired:
Previous (paid or unpaid) work within a research group in college or other institutions.Specialized SkillsRequired:
Expected to be comfortable with the wide range of statistical learning algorithms and development of simulations for empirical statistical analyses using a statistical programming language or C/C++. At least 5 of the following: R Statistical Programming Language, SPSS, SAS, Python, C/C++, and SQL, TensorFlow, Keras, PyTorch, and Apache Spark and Hive.Work Environments - Functional DemandSedentary - Very light energy level Lift 10 lbs. box overhead. Lift and carry 15 lbs. Push/pull 20 lbs. cart.Physical Activity RequirementsBending, Carry Objects, Climbing, Keyboard use/repetitive motion, Pinching/fine motor activities, Push/Pull, Reaching forward, Reaching overhead, Sitting, Squat/kneel/crawl (Squad & Knee), Standing, Talk or hear, Taste or smell, Twisting, Walking, Wrist position deviationPhysical Activity LiftingLifting (Floor to waist level) - Occasional 0-35%, Lifting (Waist level and above) - Occasional 0-35%Sensory RequirementsColor Discrimination, Depth Perception, Far Vision, Hearing, Near VisionEnvironment RequirementsAirborn Communicable Disease, Bloodborne Pathogens, Chemical, Dust Particular Matter, Extreme Noise Levels, Extreme Temperatures, Radiation, Uneven Surfaces or Elevations
#J-18808-Ljbffr
Minimum five years of experience with a Master of Science degree, or entry level experience with a Doctorate degree. Experience in the application of advanced statistical algorithms and computational and data science methods in the analyses of medical data including generalized linear models across a wide range of statistical distributions, time series analyses, and longitudinal data analyses. Familiarity with Machine-learning, Artificial Intelligence, Casual Inference or Bayesian Statistics is also required through prior coursework, publications, or informal studies of these methods. Previous experience with high-level programming languages such as SAS, R Statistical Programming Language, SPSS, C/C++, Python, and TensorFlow, Keras or PyTorch for time series and other sequence data prediction. Experience with SQL programming and data preprocessing for research studies are also required. Previous participation in research during academic training, internships, or prior job experience that resulted in one or more peer-reviewed publications.Preferred:
Prior peer-reviewed publications in PubMed-indexed journals include first or last authorship.EducationRequired:
Master of Science (with 5 years of experience) or entry-level Doctorate in the computational and data sciences including statistics, artificial intelligence, biostatistics, bioinformatics, and statistical genetics.Preferred:
Doctorate in the computational and data sciences, artificial intelligence, statistics, biostatistics, bioinformatics, and statistical genetics.TrainingRequired:
Previous (paid or unpaid) work within a research group in college or other institutions.Specialized SkillsRequired:
Expected to be comfortable with the wide range of statistical learning algorithms and development of simulations for empirical statistical analyses using a statistical programming language or C/C++. At least 5 of the following: R Statistical Programming Language, SPSS, SAS, Python, C/C++, and SQL, TensorFlow, Keras, PyTorch, and Apache Spark and Hive.Work Environments - Functional DemandSedentary - Very light energy level Lift 10 lbs. box overhead. Lift and carry 15 lbs. Push/pull 20 lbs. cart.Physical Activity RequirementsBending, Carry Objects, Climbing, Keyboard use/repetitive motion, Pinching/fine motor activities, Push/Pull, Reaching forward, Reaching overhead, Sitting, Squat/kneel/crawl (Squad & Knee), Standing, Talk or hear, Taste or smell, Twisting, Walking, Wrist position deviationPhysical Activity LiftingLifting (Floor to waist level) - Occasional 0-35%, Lifting (Waist level and above) - Occasional 0-35%Sensory RequirementsColor Discrimination, Depth Perception, Far Vision, Hearing, Near VisionEnvironment RequirementsAirborn Communicable Disease, Bloodborne Pathogens, Chemical, Dust Particular Matter, Extreme Noise Levels, Extreme Temperatures, Radiation, Uneven Surfaces or Elevations
#J-18808-Ljbffr