Data Scientist

• Work closely with the Service Recovery Modernization team to understand business needs and identify AI and machine learning opportunities.
• Collect, clean, transform, and prepare structured and unstructured data for analysis and modeling.
• Design, build, and maintain reliable data pipelines for ML and Generative AI applications.
• Develop, train, test, and validate machine learning models.
• Design and develop Generative AI applications using large language models.
• Deploy ML and GenAI solutions into production environments.
• Monitor model performance, accuracy, reliability, and data quality.
• Troubleshoot data, pipeline, and model-related issues.
• Collaborate with data engineers, software engineers, product owners, and business stakeholders.
• Document data processes, model designs, testing results, and deployment procedures.
• Follow security, privacy, governance, and responsible AI standards

Pay Range: $90 - $100

The specific compensation for this position will be determined by several factors, including the scope, complexity, and location of the role, as well as the cost of labor in the market; the skills, education, training, credentials, and experience of the candidate; and other conditions of employment. Our full-time consultants have access to benefits, including medical, dental, vision, and 401K contributions, as well as PTO, sick leave, and other benefits mandated by applicable state or localities where you reside or work.

We are seeking a Data Scientist to support the Service Recovery Modernization initiative. This resource will be embedded within the project team and will take ownership of the end-to-end development of machine learning and Generative AI solutions. The ideal candidate will have strong experience in data preparation, data pipeline development, machine learning, and deploying AI applications into production. Required:

• Experience working as a Data Scientist, Machine Learning Engineer, or in a similar role.
• Strong experience with data preparation, cleansing, transformation, and feature engineering.
• Hands-on experience designing and developing data pipelines.
• Strong programming skills in Python and SQL.
• Experience developing, testing, and deploying machine learning models.
• Experience building Generative AI or large language model applications.
• Understanding of the complete ML lifecycle, including development, validation, deployment, and monitoring.
• Experience working with structured and unstructured datasets.

Preferred:

• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Knowledge of LLM orchestration, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation.
• Experience with MLOps tools, CI/CD pipelines, model monitoring, and containerized deployment.
• Experience delivering AI solutions for customer service, operational recovery, or process modernization initiatives.
• Familiarity with data governance, model governance, and responsible AI practices.