Essential Duties & Responsibilities
The essential functions include, but are not limited to the following:
Develop and deploy traditional machine learning models, including bias modeling, risk mitigation, and predictive analytics
Build scalable ML models and end-to-end ML pipelines suitable for production environments
Perform advanced data quality assessments, validation checks, and feature identification and analysis
Evaluate bias in training data and recommend mitigation strategies aligned with responsible AI practices
Design and implement intermediate models for risk scoring and predictive insights
Apply Generative AI techniques, including prompt engineering, retrieval-augmented generation (RAG), and deployment of GenAI models
Utilize and contribute to LLM evaluation frameworks to assess performance, accuracy, and reliability
Collaborate closely with Product, Engineering, and Business teams to identify, refine, and deliver high-impact AI use cases
Prepare trained models for production, including documentation, testing, and environment replication
Monitor model performance and participate in ongoing model optimization and observability efforts
Communicate complex analyses and results to technical and non-technical stakeholders
Maintain thorough documentation and participate in continuous learning and knowledge sharing across the Data Science team
Minimum Qualifications (Knowledge, Skills, and Abilities)
Education:
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or similar quantitative field; Master’s degree preferred
Experience:
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- Strong analytical thinking and problem-solving skills
- Ability to collaborate cross-functionally with product-focused teams
Clear written and verbal communication skills
Commitment to ethical, responsible, and high-quality AI development
Big data querying experience using major cloud providers
Data processing, cleaning, feature extraction, and exploratory data analysis
Strong experience with machine learning and deep learning techniques and algorithms
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Generative AI experience, including:
Prompt engineering and prompt optimization
RAG vs. non-RAG architectures
LLM evaluation methodologies
Experience with AI agents, agentic AI systems, and multi-agent orchestration
Model training, containerization, deployment, and performance monitoring
Automation, observability, and lifecycle management of ML pipelines (MLDC / MLOps)
Proficiency in Python and common ML libraries (e.g., scikit-learn, PyTorch, TensorFlow)
Experience working with relational databases and large-scale structured and unstructured datasets
Supervisory Responsibilities
This role does not have any direct reports and is a single contributor role.
Working Environment and Travel Requirements
Work is typically in a normal office administrative environment involving minimal exposure to physical risks. Position requires little to moderate physical activity. Mostly sedentary work exerting up to 10 pounds of force occasionally or a negligible amount of force to lift, carry, push, pull, or otherwise move objects. Work involves sitting most of the time, but may involve walking or standing for brief periods of time. No significant stooping is usually required.
