BayOne Solutions
Lead Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Lead Data Scientist in Pleasanton, CA, for 6+ months at $95-$100/hr. Requires a PhD and 7-8+ years of experience, focusing on deep learning recommender systems, Python, and modern ML frameworks like PyTorch or TensorFlow.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
800
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🗓️ - Date
February 17, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Pleasanton, CA
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🧠 - Skills detailed
#Recommender Systems #Data Science #ML (Machine Learning) #Deployment #Deep Learning #PyTorch #Scala #"ETL (Extract #Transform #Load)" #TensorFlow #Python
Role description
Title: Lead Data Scientist
Location: Pleasanton CA (5 Days onsite)
Duration: 6+ months with possible extension
Payrate: $95 to $100/hr. on W2
Education level: PhD
Experience level 7-8+ Years
I’m reaching out regarding an exciting opportunity for a Lead Data Scientist to lead the redesign and optimization of large-scale recommendation systems across multiple brands.
• Analyze existing recommender system pipelines and design improved recommendation algorithms with a focus on sequence modeling and transformer-based architectures
• Lead dataset gathering, feature engineering, and development of offline models, including tuning and validation
• Build and integrate offline training pipelines with real-time inference systems for production deployment
• Collaborate cross-functionally to deliver scalable, production-grade ML systems using Python and modern ML frameworks (PyTorch or TensorFlow)
• Strong experience in deep learning–based recommender systems, multi-modal data handling, and leading hands-on technical initiatives
Title: Lead Data Scientist
Location: Pleasanton CA (5 Days onsite)
Duration: 6+ months with possible extension
Payrate: $95 to $100/hr. on W2
Education level: PhD
Experience level 7-8+ Years
I’m reaching out regarding an exciting opportunity for a Lead Data Scientist to lead the redesign and optimization of large-scale recommendation systems across multiple brands.
• Analyze existing recommender system pipelines and design improved recommendation algorithms with a focus on sequence modeling and transformer-based architectures
• Lead dataset gathering, feature engineering, and development of offline models, including tuning and validation
• Build and integrate offline training pipelines with real-time inference systems for production deployment
• Collaborate cross-functionally to deliver scalable, production-grade ML systems using Python and modern ML frameworks (PyTorch or TensorFlow)
• Strong experience in deep learning–based recommender systems, multi-modal data handling, and leading hands-on technical initiatives
