ICA, Inc.

Data Scientist

• Own the product roadmap and priorities across data engineering, data science, backend, search, and frontend teams
• Lead discovery with clients and internal stakeholders using prototypes, demos, and working software
• Translate business needs into product requirements, user stories, business rules, and measurable acceptance criteria
• Define requirements for document processing, alerts, scoring, evidence presentation, search, review workflows, and source traceability
• Partner with data scientists to define evaluation datasets, model metrics, confidence thresholds, and acceptable false-positive and false-negative rates
• Work with data engineers on ingestion, normalization, metadata, lineage, processing completeness, and data-quality requirements
• Diagnose whether product issues originate in the UI, data, model, pipeline, search, thresholds, or business rules
• Prioritize reusable platform capabilities over narrow, one-off client customizations
• Coordinate release readiness, client demonstrations, and structured feedback collection
• Measure product performance using accuracy, completeness, reviewer effort, latency, reliability, adoption, and cost
• Communicate priorities, tradeoffs, limitations, and delivery expectations clearly to clients and internal teams

Senior Product Manager, Data & AI

Backend Engineer

• Architect and Build the Shared Platform
• Define the target AWS architecture for shared data, document-processing, search, analytics, and AI capabilities.
• Design the canonical data model, ingestion and processing pipelines, storage patterns, APIs, event contracts, and serving layers.
• Build a reusable platform serving multiple products and projects with critical shared capabilities and establish repeatable implementation patterns for engineering teams.
• Develop an incremental migration and adoption strategy for bringing existing solutions onto the shared platform.
• Identify duplicated pipelines, services, infrastructure, and technical patterns across projects.
• Determine which capabilities should become shared platform services and which should remain project-specific.
• Define the Data and MLOps Architecture
• Design the AWS-based path from data science deliverables to production by establishing architecture for model packaging, deployment, serving, registry, monitoring, drift detection, and retraining workflows.
• Define onboarding standards for new data products, models, search applications, and AI-enabled services.
• Design platform capabilities supporting search indexes, embeddings, vector retrieval, RAG, and model evaluation.
• Provide Technical Design Authority
• Lead architecture and design reviews before significant development begins.
• Establish reference architectures, approved patterns, architecture decision records, and engineering standards.
• Require teams to use shared platform capabilities where appropriate and evaluate justified exceptions.
• Ensure platform designs meet performance, scalability, reliability, security, auditability, disaster-recovery, and cost requirements.
• Provide technical leadership and mentoring across data engineering, data science, software engineering, and platform teams.
• Partner with product and engineering leaders on a cross-project platform roadmap.

Principal AWS Data Platform, ML Ops Architect

Medical Device

• Build strong working relationships with client reviewers and establish a regular, structured feedback process.
• Lead discovery and validation sessions using prototypes and working software.
• Translate reviewer needs into workflows, business rules, user stories, and measurable acceptance criteria.
• Validate proposed requirements and workflows before development begins.
• Partner with the Senior Product Manager to ensure requirements are accurate from a medical-device review perspective.
• Define meaningful alert categories, priorities, reviewer actions, and escalation paths.
• Evaluate alert relevance, false positives, false negatives, evidence quality, usability, and reviewer effort.
• Test completed capabilities from a reviewer’s perspective and identify missing requirements, confusing behavior, unsupported conclusions, and workflow gaps.
• Lead client demonstrations, user acceptance testing, feedback capture, and acceptance or signoff.
• Maintain traceability from the original client's needs through requirements, validation, and approval.
• Improve domain understanding across product, engineering, data science, and leadership teams.

Senior Product Manager, Data & AI

Principal AWS Data Platform, ML Ops Architect

Medical Device Workflow & Validation Lead