Develop AI solutions: Create ML and generative AI systems for RAG pipelines, chatbots, classification, forecasting, and recommendation.
Ensure alignment with enterprise standards, seamless integration, and secure scalability.
End ‑ to ‑ End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, model change management, orchestration , observability , drift detection, and synthetic data generation.
Collaborate closely with AI engineers, data engineers, platform, security, and IT to ensure solutions are robust, maintainable , production ready, follow safety filters/guardrails, and rollback plans.
Drive execution from discovery to rollout by defining scope, milestones, and acceptance criteria; managing dependencies/risks; coordinating cross-functional workstreams; and maintaining clear status reporting, issue escalation, and delivery timelines.
Partners closely with Product, Underwriting, Distribution, Risk, Legal, and Compliance to align AI initiatives with enterprise objectives and governance expectations.
Translates complex model behavior and evaluation outcomes into clear, actionable business insights with defined success criteria (accuracy, cost, performance, reuse, ROI).

6+ years with Bachelor’s degree; less for Master’s/Ph.D.
Proficiency in Python and SQL (ideally Snowflake); experience with pandas, numpy, scikit-learn.
Strong foundation in ML, deep learning, NLP; familiarity with PyTorch/TensorFlow and generative AI.
Experience with cloud tools (Google Vertex AI, AWS SageMaker/Bedrock).
Ability to build reproducible workflows using Jupyter and GIT.
Competence in end-to-end modeling: requirements, experiment design, evaluation, production monitoring.
Experience tracking forecasting metrics (MAPE/WAPE) and LLM evaluation.
Understanding agentic AI pipelines and prompt engineering for language models.
Excellent communicator—able to translate analytics into clear business narratives for stakeholders.

Senior Applied AI Data Scientist

Key skills