Predictive modeling Work, Jobs in Cambridge, Eng (with Salaries)

Scientific Data Engineer
BIOVIA —Cambridge
- Doctoral degree
- Master's degree
- Machine learning
Associate Director Predictive Agentic Target Biology
GlaxoSmithKline —Stevenage
- Full-time
- Hybrid work
- Doctoral degree
- Master's degree
- In-person
12d
Data Scientist
FedEx EU —Stansted
- Full-time
- Hybrid work
- Master's degree
- Machine learning
- No degree
- Employee assistance programme
2d
Associate Director, PBPK-PD Translational Modeling, DMPK
GlaxoSmithKline —Stevenage
- Full-time
- Doctoral degree
- AI
11d

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Associate Director Predictive Agentic Target Biology
GlaxoSmithKline —Stevenage
Practical experience developing and applying predictive models for biological targets or cellular phenotypes. Provide timely, evidence-based recommendations to…
12d

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Scientific Data Engineer

BIOVIA -
Cambridge

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Job details

Qualifications

Data integration
Doctoral degree
Stakeholder collaboration
Data quality analysis
NumPy
R
NoSQL
Laboratory information management systems
Data and database experience
Master's degree
Pandas
Database management
ETL data integration
Machine learning
NIST standards
Machine learning modelling
Quality control
User research
Multidisciplinary collaboration
Data science
Analytics

Full job description

The BIOVIA brand of Dassault Systèmes is seeking a highly motivated and skilled Scientific Data Specialist to join our team in expanding our cutting-edge scientific data and informatics platform. This platform empowers scientists and engineers to efficiently discover and select materials, substances, and formulations based on domain knowledge, directly integrating with CAD design, multi-physical simulations, and laboratory experiments. As a Scientific Data Engineer, you will play a crucial role in curating, validating, and ensuring the quality of our scientific database, making it a valuable resource for our users.

Role Description & Responsibilities:

Data Curation and Validation: Gather, clean, and validate scientific data from diverse sources, including peer-reviewed literature, domain databases, vendor catalogs, and experimental data. Implement rigorous quality control measures to ensure data accuracy, consistency, and completeness. Focus on Material domains and future expansion to other scientific domains
Database Development and Maintenance: Contribute to the design and maintenance of our scientific ontology, ensuring efficient data storage, retrieval, domain coverage, and integration with our software platform
Pipeline Development: Implement ETL pipelines for ingesting, cleaning, and transforming scientific datasets from multiple sources
Data Analysis and Modeling: Apply statistical and machine learning techniques to analyse scientific data, identify trends, and develop predictive models for domain-relevant properties and behaviours
Ontology Development and Classification: Develop and maintain a comprehensive scientific ontology and classification system to enable efficient searching and filtering based on substance class, properties, and applications
Integration with Software Tools: Collaborate with software engineers to ensure seamless integration of the scientific database with CAD design software, multi-physical simulation tools, and laboratory information management systems (LIMS)
User Feedback and Validation: Gather feedback from users (scientists and engineers) to understand their needs and validate the usefulness and accuracy of the scientific data. Conduct user studies and analyse usage patterns to identify areas for improvement
Staying Current: Stay up-to-date with the latest advancements in relevant scientific domains, data science, and informatics to continuously improve our platform and data resources
Documentation: Maintain comprehensive documentation of data sources, validation procedures, and data models
Communication: Collaborate with global business and technical teams to understand data requirements and deliver reliable, high-quality data solutions that support downstream applications and analytics

Qualifications:

Master's or Ph.D. in Materials Science, Chemistry, Physics, Biology, Chemical Engineering, or a related scientific discipline with a strong emphasis on data analysis.
Proven experience in scientific data curation, validation, and analysis.
Strong understanding of domain-relevant properties, characterisation techniques, and substance or material selection processes.
Proficiency in data analysis and programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn), R, or similar.
Experience with database management systems (SQL or NoSQL).
Familiarity with scientific databases and ontologies (e.g., Materials Project, ChEMBL, PubChem, or ontologies developed by NIST or analogous bodies).
Experience with CAD software, multi-physical simulation tools, or LIMS is a plus.
Excellent communication, collaboration, and problem-solving skills.
Ability to work independently and as part of a global team.

What’s in it for you?

Professional Growth: Opportunity to advance within the organization
Learning Environment: Access to training, workshops, and skill development
Collaboration: Work closely with cross-functional teams
Company Culture: Work in a culture of collaboration and innovation

Inclusion statement

As a game-changer in sustainable technology and innovation, Dassault Systèmes is striving to build more inclusive and diverse teams across the globe. We believe that our people are our number one asset and we want all employees to feel empowered to bring their whole selves to work every day. It is our goal that our people feel a sense of pride and a passion for belonging. As a company leading change, it’s our responsibility to foster opportunities for all people to participate in a harmonized Workforce of the Future.

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predictive modeling jobs in cambridge, eng

Scientific Data Engineer

Associate Director Predictive Agentic Target Biology

Data Scientist

Associate Director, PBPK-PD Translational Modeling, DMPK

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