Data Scientist

Job summary

Design, build, and deploy scalable machine learning models and production-ready data products for enterprise applications. Collaborate with engineering teams to integrate models into production systems while ensuring reliability and performance through monitoring and testing.

Job details

Data Scientist II – Machine Learning & AI Employment Type: Long-Term Contract Work Arrangement: 100% Remote About the Opportunity We are hiring a Data Scientist II on behalf of one of our established U.S.-based clients for a long-term remote contract opportunity. This role sits within a data product environment and is focused on designing, building, and deploying machine learning models and production-ready data products that support enterprise initiatives. This is a hands-on, engineering-oriented Data Science position. The successful candidate will translate complex business problems into scalable, data-driven solutions and take models beyond experimentation into real production environments. The ideal candidate has practical experience building and operationalizing machine learning models, strong Python and SQL skills, and experience working within modern cloud environments. Exposure to LLMs, Retrieval-Augmented Generation (RAG), NLP, and modern AI platforms is highly desirable. Key Responsibilities Design, build, train, evaluate, and deploy machine learning models and data products for enterprise applications. Analyze complex datasets to identify opportunities for product, operational, and business improvements. Translate business and operational requirements into scalable data science and machine learning solutions. Perform feature engineering, data preparation, exploratory analysis, and model development. Develop models using techniques such as classification, regression, clustering, NLP, optimization, and neural networks. Build production-ready solutions rather than limiting work to exploratory analysis or reporting. Contribute to advanced AI initiatives involving LLMs, RAG, NLP, and hybrid modeling approaches. Develop and improve frameworks and tools for automated data collection and processing. Collaborate with software engineering teams to integrate models into production systems using APIs, pipelines, and cloud services. Support deployment and lifecycle management of models using platforms such as Azure Machine Learning, AWS SageMaker, or comparable technologies. Contribute to the design of data products, including model outputs, APIs, and downstream integrations. Perform model validation, testing, monitoring, and documentation to ensure reliability and reproducibility. Develop and support A/B testing frameworks and methods for evaluating model quality. Participate in technical design discussions and contribute to architecture and implementation decisions. Work within established software development lifecycle (SDLC) practices, including version control, testing, code reviews, and release processes. Clearly communicate model behavior, assumptions, limitations, and results to both technical and non-technical stakeholders. Stay current with emerging AI, machine learning, and data science technologies. Required Qualifications Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or another relevant technical discipline. Approximately 5+ years of professional experience in Data Science, Machine Learning, Applied AI, or a closely related field. Strong hands-on experience with Python. Strong working knowledge of SQL and relational data. Solid foundation in machine learning, statistical modeling, and applied data science. Demonstrated experience developing and deploying machine learning models into production environments. Practical understanding of feature engineering, model evaluation, validation, and experimentation. Experience with machine learning techniques such as classification, regression, clustering, decision trees, neural networks, NLP, and related approaches. Experience working with Azure, AWS, or another major cloud platform. Familiarity with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or equivalent. Understanding of data pipelines, APIs, and production integration patterns. Experience working within structured software development environments and SDLC practices. Strong analytical and problem-solving abilities. Strong written and verbal communication skills. Preferred Qualifications Master’s degree in a relevant technical discipline. Hands-on experience with Large Language Models (LLMs). Experience developing Retrieval-Augmented Generation (RAG) solutions. Experience with Natural Language Processing (NLP). Exposure to Azure OpenAI, AWS Bedrock, or comparable AI platforms. Experience integrating ML or AI solutions into enterprise software products. Experience building APIs or working closely with software engineering teams on model deployment. Experience with MLOps, model monitoring, CI/CD, or automated ML deployment pipelines. What We Are Looking For We are particularly interested in candidates who have taken machine learning solutions from development into real-world production environments. This is not primarily a reporting, dashboarding, or purely analytical Data Scientist position. Candidates should be comfortable writing production-quality Python, working with engineering teams, deploying models, and taking ownership of technical solutions. Experience with modern Generative AI technologies such as LLMs and RAG is valuable, but candidates should also have a strong foundation in traditional machine learning and statistical modeling. Contract Details Long-term contract opportunity 100% remote Work with an established U.S.-based enterprise client Collaborate with experienced Data Science, Product, and Engineering teams Opportunity to work on production ML, AI, and enterprise data products Candidates must be based in Canada and legally able to work as an independent contractor in Canada.

What you’ll do

Design, build, and deploy scalable machine learning models and production-ready data products for enterprise applications. Collaborate with engineering teams to integrate models into production systems while ensuring reliability and performance through monitoring and testing.

Requirements

Requires a bachelor's degree in a technical field and approximately 5+ years of professional experience in Data Science or Applied AI. Candidates must possess strong Python and SQL skills, along with practical experience in deploying machine learning models within cloud environments.

Listed skills

• Microsoft Azure · Preferred
• SQL · Preferred
• Machine learning · Preferred
• Amazon Web Services · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• Machine Learning
• Data Science
• Cloud Computing
• Azure
• AWS
• LLMs
• RAG
• NLP
• Feature Engineering
• Model Deployment
• Statistical Modeling
• APIs
• MLOps
• SDLC

Job areas

• Data & Analytics
• Software
• Technology
• Engineering
• Science & Research

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