Job Summary
This role focuses on designing configuring and sustaining execution systems that integrate manufacturing execution platforms with advanced machine learning and large language model concepts for bioinformatics centric environments. The candidate will apply expertise in MES generic capabilities Tulip overview GDP and GMP essential practices to deliver compliant efficient and data driven workflows while collaborating closely with cross functional teams in an office based day shift setting to s
Responsibilities
- Design and implement execution system solutions that integrate MES generic capabilities with machine learning driven decision support to improve operational efficiency in bioinformatics oriented manufacturing environments.
- Configure Tulip overview based workflows to orchestrate standardized procedures that ensure traceable and consistent execution of critical manufacturing and analytical activities aligned with GDP and GMP essential expectations.
- Develop and optimize machine learning models that leverage bioinformatics datasets to support predictive insights anomaly detection and continuous improvement of execution processes within compliant system landscapes.
- Apply concepts of LLM to design intelligent assistance features within execution systems that enable context aware guidance documentation support and knowledge retrieval for operational teams.
- Coordinate with process owners quality experts and information technology specialists to gather requirements translate them into detailed system specifications and validate that configurations align with regulated practices.
- Prepare comprehensive system documentation including requirement descriptions design overviews configuration records and test evidence to demonstrate adherence to GDP and GMP essential standards.
- Execute unit tests integration tests and user acceptance evaluations to verify that MES generic capabilities Tulip overview flows and machine learning components function as intended across bioinformatics use cases.
- Troubleshoot complex incidents by analyzing execution logs machine learning outputs and data flows then implement corrective actions that address root causes and prevent recurrence.
- Monitor system performance indicators and data quality metrics to ensure stable reliable and compliant operation of execution platforms within an office based day shift model.
- Collaborate with cross functional stakeholders to identify opportunities where automation data driven insights and intelligent assistance can reduce manual effort and enhance operational resilience.
- Contribute to continuous improvement initiatives by proposing enhancements that simplify workflows reduce error rates and align execution practices with evolving regulatory expectations in bioinformatics domains.
- Provide structured user enablement by preparing reference materials walkthroughs and demonstrations that help operational personnel effectively use MES generic functions Tulip overview screens and analytics features.
- Ensure that all configuration activities change implementations and validation exercises strictly follow GDP and GMP essential guidelines to protect data integrity and patient safety outcomes.
Qualifications
- Describe prior experience of at least four years in configuring and supporting MES generic platforms with a strong understanding of Tulip overview capabilities and their application in regulated environments.
- Demonstrate applied knowledge of machine learning and concepts of LLM in the context of bioinformatics workflows including handling specialized datasets and implementing responsible model practices.
- Exhibit familiarity with GDP and GMP essential principles including documentation discipline traceability expectations and the ability to operate within audited system landscapes.
- Show experience working within bioinformatics or life sciences domains where execution systems interface with laboratory processes analytical pipelines or clinical manufacturing operations.
- Highlight capability to analyze complex technical problems design pragmatic solutions and communicate findings clearly to cross functional teams in a concise and structured manner.
- Display comfort with office based collaboration models including participation in day shift support rotations design workshops and formal change control discussions.
- Illustrate an inclusive mindset strong attention to detail and commitment to quality outcomes that contribute to the organization purpose of improving healthcare and societal well being through reliable and compliant digital systems.
Certifications Required
Preferred certifications include MES specialist credential GMP practitioner certification or a recognized machine learning or data science certification relevant to life sciences domains
Over Cognizant
Cognizant (NASDAQ: CTSH) is een bouwer van AI-oplossingen en een leverancier van technologiediensten. Wij slaan de brug tussen AI-investeringen en ondernemingswaarde door het bouwen van full-stack AI-oplossingen voor onze klanten. Onze diepgaande kennis van sectoren, processen en engineering stelt ons in staat om de unieke context van een organisatie te verankeren in technologische systemen. Deze systemen versterken het menselijk potentieel, realiseren tastbare resultaten en geven wereldwijde ondernemingen een voorsprong in een snel veranderende wereld. Ontdek hoe op cognizant.ai of @cognizant.
Aanvullende arbeidsinformatie
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Van sollicitanten kan worden verwacht dat zij gesprekken bijwonen, persoonlijk of via een videogesprek. Daarnaast kan van kandidaten worden gevraagd om tijdens elk gesprek een geldig, door de overheid uitgegeven identiteitsbewijs (zoals een identiteitskaart of paspoort) te tonen.
Cognizant is een werkgever die gelijke kansen biedt. Je sollicitatie en kandidatuur worden niet beoordeeld op basis van ras, huidskleur, geslacht, religie, levensovertuiging, seksuele geaardheid, genderidentiteit, nationale afkomst, handicap, genetische informatie, zwangerschap, veteranenstatus of enige andere eigenschap die wordt beschermd door federale, regionale of lokale wetgeving.
