Digital Biomarker Biomedical Engineer
W skrócie
Digital Biomarker Biomedical Engineer in Warszawa (PL). Responsibilities include signal processing, algorithm validation, data analysis, and documentation. Requires PhD, Python, SciPy/NumPy/pandas, and validation experience.
Słowa kluczowe
Skrót przygotowany przez AI na podstawie treści ogłoszenia.
Focus: Provide signal processing expertise to support algorithm validation, investigate data discrepancies, and help establish confidence in system equivalency during platform or environment transitions.
Responsibilities:-Provide technical guidance on signal processing to migration, engineering, and clinical teams, ensuring a shared understanding of algorithm behaviour and expected output characteristics.-Analyse datasets to identify deviations, edge cases, and anomalies, including those introduced by changes in compute environments or data pipelines. Apply appropriate statistical methods to assess whether discrepancies are meaningful.-Deconstruct the logic of existing signal processing algorithms and produce clear, well-structured documentation to support computer system validation (CSV) efforts and enable knowledge transfer across teams.-Contribute to the design of validation frameworks and test cases, including the definition of tolerance thresholds and acceptance criteria for comparing algorithm outputs across environments.-Develop and execute CSV-based comparison workflows to verify the consistency of algorithmic output. Identify, classify, and quantify discrepancies, distinguishing between acceptable numerical precision variation and genuine regressions.-Collaborate with data migration specialists and engineers to resolve data-integrity issues. Ensure that all deviations are properly quantified, documented, and traceable for review and sign-off.
Qualifications:-PhD in Electrical Engineering, Applied Mathematics, Physics, Data Science, or a related quantitative field.-Solid understanding of digital signal processing and time-series analysis.-Proficiency in Python, including familiarity with libraries like SciPy, NumPy, or pandas.-Experience with algorithm or software validation, including defining acceptance criteria and tolerance thresholds. Formal V& V experience is preferred, but equivalent structured testing experience is acceptable.-Experience developing data comparison or reconciliation workflows using large tabular or CSV datasets.-Demonstrated ability to produce clear technical documentation for diverse audiences.-Able to communicate complex technical concepts to stakeholders outside your speciality.
Preferred:-Familiarity with database migrations or platform equivalency testing.-Exposure to version-controlled analysis workflows or reproducible research practices.-Experience with high-frequency sensor data.
| Opublikowana | 2026-08-24 |
| Wygasa | 2026-10-31 |
| Źródło |
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