Date Posted: Sept. 16, 2022 Domestic relocation as well as Visa sponsorship is available. Required qualifications: Minimum of a Bachelor's degree with five plus years of experience or Masters degree with two plus years' experience or PhD; Educational preparation or applied experience in at least one of the following areas: Machine Learning, Computational Biology, Applied Mathematics, Bioinformatics, Genomics, Computer Science, Statistics, Biostatistics, or other related quantitative discipline; Demonstrate proficiency applying deep learning, machine learning algorithms, concepts to image classification, object detection, and object segmentation; Skilled in computational skills and level of experience building models using Python and building analysis pipelines; Understanding of software development best practices (version control, code documentation & review, cloud-based sequence analysis, database management); Intermediate proficiency in predictive modeling including comprehension of theory, identification strategies, limitations, and pitfalls; Experience in successful delivery of valuable analysis through application of domain knowledge; Possess strong business acumen; Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner. Entirely remote jobs that could be performed in Colorado: employees can expect to be paid a salary of approximately $120,000 (or between $110,000 to $135,000). Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc..

Pfizer Inc. is an American multinational pharmaceutical and biotechnology corporation headquartered on 42nd Street in Manhattan, New York City. Pfizer develops and produces medicines and vaccines for immunology, oncology, cardiology, endocrinology, and neurology. The company has several blockbuster drugs or products that each generate more than US$1 billion in annual revenues.

Deep learning methods are the gold standard for non-linear statistical modeling in computer vision and in natural language processing but are rarely used in psychometrics. To bridge this gap, we present a novel deep learning algorithm for exploratory item factor analysis (IFA). Our approach combines a deep artificial neural network (ANN) model called a variational autoencoder (VAE) with recent work that uses regularization for exploratory factor analysis. We first provide overviews of ANNs and VAEs. We then describe how to conduct exploratory IFA with a VAE and demonstrate our approach in two empirical examples and in two simulated examples. Our empirical results were consistent with existing psychological theory across random starting values. Our simulations suggest that the VAE consistently recovers the data generating factor pattern with moderate-sized samples. Secondary loadings were underestimated with a complex factor structure and intercept parameter estimates were moderately biased with both simple and complex factor structures. All models converged in minutes, even with hundreds of thousands of observations, hundreds of items, and tens of factors. We conclude that the VAE offers a powerful new approach to fitting complex statistical models in psychological and educational measurement.

The Linux Foundation launched a deep learning initiative this week designed to create a "neutral space for harmonization and acceleration" of AI, machine learning and deep learning technologies. The initial focus will be an AI standardization effort. The open source project called LF Deep Learning Foundation seeks to make the emerging AI technologies widely available to data scientists and developers, the group said during this week's Open Networking Summit in Los Angeles. Founding members include several Chinese technology giants that are pouring huge sums into AI research as part of a national strategy to dominate AI. They are: China's search giant Baidu; networking giant Huawei (SHE: 002502); Tencent, often referred to as China's Facebook; and telecommunications equipment vendor ZTE (SHE: 000063).