

Senior Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Scientist with 7–12 years of experience, based in San Jose, California. It offers a C2C contract and requires expertise in Python, AWS SageMaker, and telecom fraud detection. A Bachelor's degree in a related field is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
July 1, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
Corp-to-Corp (C2C)
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🔒 - Security clearance
Unknown
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📍 - Location detailed
San Jose, CA
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🧠 - Skills detailed
#AWS SageMaker #Data Science #AWS (Amazon Web Services) #Langchain #Automation #Compliance #Batch #OpenSearch #SageMaker #TensorFlow #Databricks #Spark (Apache Spark) #Anomaly Detection #Elasticsearch #Big Data #Computer Science #AI (Artificial Intelligence) #GIT #Python #MLflow #Databases #Consulting #Kafka (Apache Kafka) #PyTorch #Azure #Docker
Role description
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Who We Are
Artmac Soft is a technology consulting and service-oriented IT company dedicated to providing innovative technology solutions and services to customers.
Job Description
Job Title : Senior Data Scientist
Job Type : C2C
Experience : 7–12 Years
Location : San Jose , California (On-Site)
Required Skills
• Strong proficiency in Python, with experience in PyTorch, TensorFlow, and Scikit-learn.
• Expertise in AWS SageMaker, Bedrock, and OpenSearch/Elasticsearch.
• Experience with LangChain, LangGraph, LlamaIndex, and vector stores.
• Hands-on with Docker, MLflow, Git, and Databricks.
• Knowledge of Spark, Kafka, Azure Event Hub, and big data ecosystems.
• Strong experience in telecom, fraud detection, or anomaly detection use cases.
Responsibilities
• Architect and deploy LLM-based solutions using AWS Bedrock, SageMaker, and Docker.
• Develop and optimize Retrieval Augmented Generation (RAG) pipelines using vector databases such as OpenSearch and Elasticsearch.
• Build multi-agent systems and business automation flows using LangChain and LangGraph.
• Fine-tune models such as Claude, LLaMA, Titan, and others based on business use cases.
• Implement GPU-optimized model training workflows using parallelism and dynamic batching.
• Collaborate with cross-functional teams to drive GenAI use cases from POC to production.
• Ensure AI safety and compliance using tools like Bedrock guardrails.
• Maintain MLOps practices with MLflow, Databricks, and CI/CD pipelines.
Qualifications
• Bachelor's degree in Engineering, Computer Science, or related field.