Description Vi manages a petabyte scale data lakehouse that drives data and ML pipelines across all our products. Vi is looking for an engineer with deep expertise building and scaling big-data and ML pipelines. The role
Everest Clinical Research (“Everest”) is a full-service contract research organization (CRO) providing a broad range of expertise-based clinical research services to worldwide pharmaceutical, biotechnology, and medical device industries. We serve some of the best-known companies and
Everest Clinical Research (“Everest”) is a full-service contract research organization (CRO) providing a broad range of expertise-based clinical research services to worldwide pharmaceutical, biotechnology, and medical device industries. We serve some of the best-known companies and
ROLE SUMMARY As a Senior Cheminformatics Data Scientist within the Computational Absorption, Distribution, Metabolism, and Excretion (cADME) group in Pfizer’s Pharmacokinetics, Dynamics, and Metabolism (PDM) organization, you will apply data-driven approaches to address critical challenges in
Role Summary/Purpose: The AVP, Acquisition Fraud Strategy and Model Monitoring, is a multi-functional role within credit fraud acquisitions strategy team. The primary responsibilities include overseeing the performance of fraud models and conducting in-depth data analytics to
Job Description Pay Range: $107,000.00 - $1370,000.00 / year Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also
Essential Duties and Responsibilities: - Perform hands-on data analysis and modeling with huge data sets. - Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms. - Work side-by-side with
Essential Duties and Responsibilities: - Perform hands-on data analysis and modeling with huge data sets. - Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms. - Work side-by-side with