Data Scientist II

We have an exciting opportunity for a Data Scientist within our data product space. This individual will be focused on designing, building, and deploying machine learning models and data products that support our enterprise initiatives. This role focuses on developing scalable, production ready solutions by translating complex business problems into data-driven approaches and model-based outputs.

Working closely with product managers, engineering teams, and business stakeholders, this position contributes to the development of data products from concept through deployment, ensuring solutions are reliable, performant, and aligned with real world use cases. The role includes hands on model development, feature engineering, and integration into production systems within cloud environments.

The ideal candidate has experience building and operationalizing machine learning models and is comfortable working with modern AI techniques, including large language models (LLMs) and retrieval-augmented generation (RAG), where applicable. Experience with platforms such as Azure, AWS, or similar ecosystems is strongly preferred.

Success in this role requires strong technical expertise, problem-solving skills, and the ability to deliver high-quality solutions within a structured development environment. This role focuses on building and deployment of production data products and is not limited to exploratory analysis or reporting.

This position is open to remote or hybrid.

• Mine and analyze data from internal databases to drive optimization and improvement of product development and business strategies
• Creating new, experimental frameworks to collect data
• Building tools to automate data collection
• Develop custom data models and algorithms to apply to data sets
• Design, build, train, and deploy machine learning models and data products for enterprise use
• Translate business and operational needs into scalable data science solutions and modeling approaches
• Perform feature engineering, data preparation, and exploratory analysis to support model development
• Develop and evaluate models using appropriate techniques (e.g., classification, regression, NLP, optimization)
• Contribute to the design of data products, including model outputs, APIs, and integration into downstream systems
• Support advanced AI use cases, including LLM-based solutions, retrieval-augmented generation (RAG), and hybrid modeling approaches where appropriate
• Collaborate with engineering teams to integrate models into production environments using APIs, pipelines, and cloud services
• Support deployment and lifecycle management of models within Azure Machine Learning, AWS, or similar platforms
• Perform model validation, testing, and documentation to ensure quality and reproducibility
• Contribute to technical design discussions and provide input on architecture and implementation strategies
• Work within the full software development lifecycle (SDLC), including version control, testing, and release processes
• Communicate model behavior, assumptions, and results clearly to technical and non-technical stakeholders
• Develop A/B testing framework and test model quality
• Passion for technology and emerging AI/ML trends
• Additional duties as assigned

• Strong problem-solving skills with an emphasis on product development.
• Strong foundation in machine learning, statistical modeling, and data science techniques
• Experience building and deploying machine learning models in production environments
• Familiarity with modern AI approaches, including:
  Natural language processing (NLP) Large language models (LLMs) Retrieval-Augmented Generation (RAG) Feature engineering and model evaluation techniques
• Experience working with cloud platforms such as Azure, AWS, or similar ecosystems
• Familiarity with data pipelines, APIs, and integration patterns
• Proficiency in programming languages such as Python and database management including SQL
• Strong problem-solving skills with the ability to structure complex problems into analytical solutions
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks,…