Overview
Skills
Job Details
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).