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).
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