Key Responsibilities

Converter Strategy & Validation: Define converter scopes, acceptance criteria, test scenarios, and release readiness requirements aligned with internal SDLC, data governance, and Quality Engineering standards.
Requirements & Semantic Mapping: Lead discovery sessions with Scientists and SMEs to capture key data fields, partner with Semantic Engineers to map instrument outputs to Allotrope Ontologies, and oversee vendor development.
Quality Oversight & Defect Triage: Guide the Quality Engineering squad through source-to-target data validation, sample dry runs, defect resolution, Jira traceability, and audit readiness.
Cross-Functional Leadership: Facilitate communication across Business, Product, Engineering, and Vendor teams to manage dependencies, resolve delivery risks, and mentor team members in best practices.
Continuous Improvement: Drive process standardization by creating reusable validation assets, improving documentation quality, and embedding risk-based decision-making across the converter lifecycle.

Required Skills & Experience

Background: Bachelor's or Master's degree in Computer Science, Data Science, Biotechnology, or a related field (or equivalent experience).
Data & Quality Expertise: 6+ years in Data Engineering, Integration, or Management with strong hands-on experience in data modeling, mapping, transformation, data quality assurance, and traceability.
Lifecycle & Governance: Deep understanding of the Software Development Life Cycle (SDLC), data governance principles, and data lifecycle management.
Leadership & Collaboration: Proven track record leading technical teams, removing delivery blockers, managing project risks, and translating complex scientific/business needs into scalable technical solutions.
Communication: Exceptional analytical, problem-solving, facilitation, and stakeholder management skills in cross-functional environments.

Research Scientist, Kaggle

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