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Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist in Newark, NJ (Hybrid – 3 days onsite). Contract length is W2 only, C2H. Requires 5-9 years of experience, expertise in AI/ML, Python, SQL, and database management, along with strong leadership and problem-solving skills.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
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🗓️ - Date
April 2, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Hybrid
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
New Jersey, United States
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🧠 - Skills detailed
#SageMaker #Data Science #Data Analysis #Cloud #Agile #A/B Testing #ML (Machine Learning) #"ETL (Extract #Transform #Load)" #Jenkins #AWS (Amazon Web Services) #Computer Science #Database Management #Database Schema #Statistics #Databases #Monitoring #AWS SageMaker #Azure #Visualization #Python #Programming #SQL (Structured Query Language) #Cloudbees #Deployment #Mathematics #Model Deployment #AI (Artificial Intelligence)
Role description
Role: Data Scientist
Location: Newark, NJ (Hybrid – 3 Days onsite a week)
Experience: 5-9 Years
W2 Only - C2H
The Skills and expertise you bring:
• Applied Statistics, Computer Science, or Engineering or experience in related fields with a focus on machine learning, AI, and LLMs.
• Junior category industry experience with responsibility for developing and delivering advanced quantitative, AI/ML, analytical and statistical solutions.
• Ability to lead a small team with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization to deliver AI products.
• Ability to influence business stakeholders and to drive adoption of AI/ML solutions.
• Experience with agile development methodologies, Test-Driven Development (TDD), and product management.
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
• Demonstrated ability to mentor and operational management of data science team based on project requirements, resourcing requirements, and planning dependencies as appropriate, anticipate risks and bottlenecks and proactively takes actions
• Excellent problem solving, communication and collaboration skills, and stakeholder management
Experience and/or deep expertise with several of the following:
• Machine Learning and AI: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, interpreting, and monitoring machine learning models. Expertise in traditional machine learning models (unsupervised, XGBoost, etc.) and Large Language Models (OpenAI, Claude).
• Model Deployment: Understanding of model development life cycle, CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness, etc.), A/B testing, and pipeline frameworks such as AWS SageMaker, and newer AWS/Azure Agentic AI infrastructure products.
• Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.
• Database Management Systems: Knowledge of how databases are structured and function in order use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.
• Data Analysis and Insights: Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.
• Programming Languages: Python and SQL
Role: Data Scientist
Location: Newark, NJ (Hybrid – 3 Days onsite a week)
Experience: 5-9 Years
W2 Only - C2H
The Skills and expertise you bring:
• Applied Statistics, Computer Science, or Engineering or experience in related fields with a focus on machine learning, AI, and LLMs.
• Junior category industry experience with responsibility for developing and delivering advanced quantitative, AI/ML, analytical and statistical solutions.
• Ability to lead a small team with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization to deliver AI products.
• Ability to influence business stakeholders and to drive adoption of AI/ML solutions.
• Experience with agile development methodologies, Test-Driven Development (TDD), and product management.
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
• Demonstrated ability to mentor and operational management of data science team based on project requirements, resourcing requirements, and planning dependencies as appropriate, anticipate risks and bottlenecks and proactively takes actions
• Excellent problem solving, communication and collaboration skills, and stakeholder management
Experience and/or deep expertise with several of the following:
• Machine Learning and AI: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, interpreting, and monitoring machine learning models. Expertise in traditional machine learning models (unsupervised, XGBoost, etc.) and Large Language Models (OpenAI, Claude).
• Model Deployment: Understanding of model development life cycle, CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness, etc.), A/B testing, and pipeline frameworks such as AWS SageMaker, and newer AWS/Azure Agentic AI infrastructure products.
• Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.
• Database Management Systems: Knowledge of how databases are structured and function in order use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.
• Data Analysis and Insights: Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.
• Programming Languages: Python and SQL
