Dice is the leading career destination for tech experts at every stage of their careers. Our client, HireBlazer, is seeking the following. Apply via Dice today! Job Title: Sr. Data Scientist Location: Irvine, CA (Hybrid - Onsite and Remote) or San Francisco Market St (Onsite) or Telecommute (Remote) Contract Type: Contract to Hire Project Overview: The Sr. Data Scientist will join the Personalization Data Science and Machine Learning team to focus on solving recommendations, ranking, user condition predictions, and search problems. This KPI-driven team leverages Machine Learning (ML) to deliver personalized experiences. The role involves building end-to-end solutions, collaborating with data scientists and engineers, and ensuring engineering excellence with solid production releases. The team utilizes state-of-the-art machine learning and strives for low-latency solutions. Top Responsibilities: • Apply advanced statistical and predictive modeling techniques to optimize healthcare and digital experiences. • Propose innovative solutions using data mining, statistical analysis, and machine learning. • Support business needs related to analytics, predictive modeling, and business intelligence. • Collaborate effectively with internal clients to translate their needs into data science use cases. • Provide ongoing tracking and monitoring of model performance and recommend improvements to methods and algorithms. Required Qualifications: • Bachelor's Degree (Minimum Education Requirement). • Strong hands-on skills in Data Analytics and ML-Ops. • Ability to turn state-of-the-art research into production-level code. • Experience developing analytics with machine learning, deep learning, NLP, and/or other related modeling techniques. • Proficiency in Python, TensorFlow, PyTorch, and/or PySpark. • Ability to translate business needs and requirements into technical solutions. • Solid analytical and problem-solving skills. Preferred Qualifications: • Master's or Ph.D. degree in Computer Science, Applied Mathematics, (Bio) Statistics, Applied Statistics, Economics, or similar quantitative fields. • Experience developing and deploying models related to recommender systems, NLP, and time series forecasting. • Experience developing algorithms for search engines (e.g., name entity recognition, intent classification, spell correction, auto-completion), cold-start recommendation, and semi-supervised learning (e.g., positive unlabeled learning).