Talent Groups
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with a Master's or PhD in a quantitative field, requiring 5+ years of experience in data science projects, strong Python or R skills, and expertise in machine learning frameworks. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
May 5, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Pennsauken, NJ
-
🧠 - Skills detailed
#AI (Artificial Intelligence) #Supervised Learning #Mathematics #Cloud #Azure #GCP (Google Cloud Platform) #Data Science #TensorFlow #Computer Science #Spark (Apache Spark) #Data Engineering #A/B Testing #Data Pipeline #NoSQL #Databases #Statistics #Docker #Hadoop #NLP (Natural Language Processing) #Python #Scala #Unsupervised Learning #R #Programming #Version Control #Monitoring #ML (Machine Learning) #Deep Learning #AWS (Amazon Web Services) #Kubernetes #PyTorch #SQL (Structured Query Language) #Kafka (Apache Kafka) #Regression
Role description
• Master’s or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field
• Specialization in Machine Learning, Artificial Intelligence, Cognitive Science, or Data Science preferred
• 5+ years of hands-on experience delivering end-to-end data science projects with measurable impact on clinical or business outcomes
• Strong programming expertise in Python or R, with experience in machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch
• Solid foundation in machine learning and statistical methods, including supervised and unsupervised learning, deep learning, NLP, computer vision, regression, ensemble models, and A/B testing
• Experience in data engineering, including SQL/NoSQL databases, distributed systems (Hadoop, Spark, Kafka), and building scalable data pipelines on cloud platforms (AWS, Azure, or GCP)
• Proven experience in deploying and maintaining ML models, including MLOps practices such as containerization (Docker, Kubernetes), model monitoring, and version control
• Master’s or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field
• Specialization in Machine Learning, Artificial Intelligence, Cognitive Science, or Data Science preferred
• 5+ years of hands-on experience delivering end-to-end data science projects with measurable impact on clinical or business outcomes
• Strong programming expertise in Python or R, with experience in machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch
• Solid foundation in machine learning and statistical methods, including supervised and unsupervised learning, deep learning, NLP, computer vision, regression, ensemble models, and A/B testing
• Experience in data engineering, including SQL/NoSQL databases, distributed systems (Hadoop, Spark, Kafka), and building scalable data pipelines on cloud platforms (AWS, Azure, or GCP)
• Proven experience in deploying and maintaining ML models, including MLOps practices such as containerization (Docker, Kubernetes), model monitoring, and version control
