Generative AI & RAG

• Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that ground digital agents in relevant enterprise and customer information.
• Develop and evaluate embeddings, retrieval strategies, ranking approaches, semantic search, and other knowledge-retrieval techniques.
• Build and refine end-to-end pipelines that combine LLMs with retrieval systems, enterprise knowledge sources, and agent workflows.
• Develop approaches to improve the relevance, accuracy, and consistency of AI-generated responses.
• Identify and mitigate issues such as hallucinations, poor retrieval, inappropriate responses, and other failure modes in generative AI applications.

Evaluation & Experimentation

• Develop rigorous evaluation frameworks for measuring digital agent quality, including accuracy, relevance, task completion, reliability, safety, and customer experience.
• Design offline and online experiments to compare models, prompts, retrieval strategies, agent configurations, and other AI approaches.
• Apply statistical analysis, hypothesis testing, segmentation, and other quantitative methods to evaluate AI performance and identify opportunities for improvement.
• Define appropriate metrics and benchmarks that connect model and agent performance to customer and business outcomes.
• Analyze production behavior and feedback to identify patterns, failure modes, and opportunities to continuously improve digital agents.
• Ensure data and model quality through comprehensive testing, validation, and performance evaluation.

Data & Solution Development

• Gather, preprocess, analyze, and model large volumes of structured and unstructured data from multiple sources.
• Use Python, SQL, Spark, and other relevant technologies to build scalable analytical and machine learning solutions.
• Develop features, datasets, and analytical approaches that support model development, experimentation, and evaluation.
• Partner with data and engineering teams to build and maintain reliable pipelines for model training, evaluation, and production use.
• Contribute to scalable ML/AI pipelines that support experimentation, deployment, monitoring, and continuous improvement.
• Ensure solutions are reproducible, maintainable, and designed to operate effectively at production scale.

Collaboration

• Partner with Product, AI/ML Engineering, Software Engineering, and other Data Science team members to translate customer and business problems into effective AI solutions.
• Communicate analytical findings, model performance, tradeoffs, and recommendations clearly to technical and non-technical stakeholders.
• Participate in technical and design reviews and help establish strong practices for AI experimentation, evaluation, and model development.
• Mentor and support less experienced data scientists and contribute to knowledge sharing across the team.

What You Will Bring To The Role

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Machine Learning, Analytics, or a related quantitative field; Master's degree preferred.
• 4+ years of professional experience in data science, machine learning, applied AI, or a related analytical field.
• Strong experience developing and applying supervised and unsupervised machine learning models to real-world problems.
• Hands-on experience with generative AI, large language models, NLP, or other modern AI technologies.
• Strong programming skills in Python and experience with SQL and large-scale data processing technologies.
• Experience with modern machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies.
• Strong understanding of statistical analysis, experimentation, hypothesis testing, model evaluation, and performance measurement.
• Experience working with large volumes of structured and unstructured data.
• Demonstrated ability to independently frame ambiguous problems, develop analytical approaches, evaluate alternatives, and deliver actionable solutions.
• Experience taking machine learning or AI solutions beyond experimentation and contributing to their deployment and operation in production environments.
• Understanding of model quality, data quality, bias, reliability, and other considerations associated with production AI systems.
• Strong problem-solving and analytical skills with the ability to connect technical results to customer and business outcomes.
• Strong written and verbal communication skills with the ability to explain complex analytical and AI concepts to technical and non-technical audiences.
• Ability to collaborate effectively across Data Science, AI/ML Engineering, Software Engineering, Product, and business teams.

Diversity, Inclusion, and Equal Opportunity

We hire, promote, and compensate employees based on their ability to perform their job responsibilities, without regard to race, color, creed, religion, sex, gender, marital status, national origin, ancestry, age, citizenship, physical or mental disability, sexual orientation, or any other basis protected by applicable law (collectively referred to in our Code of Conduct as “Protected Classes”). We do not tolerate employment discrimination in the workplace, and we are committed to making reasonable accommodations for identified disabilities or other limitations as required by all applicable laws. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.