29 D Modeling jobs in the United Kingdom

The Cadillac Formula 1® Team is what happens when history, purpose, and daring talent come together. Backed by TWG Global and GM, our team is uniquely positioned to disrupt Formula 1® , bringing a fresh perspective and an unrelenting drive for success. We have the energy of a start-up, with the ideas and originality of a business that always wants to lead, never wants to follow.

We’re building everything from the ground up, from a high-performance car to an inclusive, values-driven culture. We show bold ambition. We combine leadership in innovation with excellence in execution. We are one team. We have the freedom to think differently, the opportunity to shape processes and practices, and an ego-free environment where people thrive on being challenged by those around them. A historic name behind us. Career-defining moments ahead.

A New Chapter Begins.

Fueled by bold ambition
Play your part in getting us on the grid.

Closing Date: 12th September 2025

As a senior member of the engineering team, you will lead the dynamic modeling of the complete F1® gearbox system, hydraulic assemblies, clutch, brake-by-wire, and other chassis systems. You will:

• Provide requirements for complex design projects
• Act as a key technical interface between design, vehicle performance, and power unit operations
• Mentor junior engineers by sharing your expert knowledge of dynamic modeling platforms (such as Dymola and Simulink)
• Collaborate closely with the vehicle performance department to develop effective drivetrain models
• Champion continuous improvement and innovative analysis techniques

Requirements

Driven by high performance
What do you need to bring to the team?

This is an opportunity for someone who combines vision, leadership and elevated technical knowledge. You must have:

• Significant experience in the development of high-performance gearbox or drivetrain systems in Formula 1®, WEC, Indy Car or an equivalent motorsport environment
• A proven track record in leading complex modeling projects from concept through to trackside validation
• A Bachelor’s or Master’s degree in Engineering, Automotive Engineering or related discipline, or equivalent
• Strong working knowledge of FIA technical regulations relating to transmission systems
• Experience of power unit control and torque arbitration would be ideal

A team like no other.

The Cadillac Formula 1® Team challenges conventions and redefines success through bold ambition, cutting-edge innovation, and an unwavering commitment to precision and excellence—on and off the track. This includes offering industry-leading pension, generous time off and, as part of a global brand, huge potential for career development.

As an equal opportunities employer, we are committed to the equal treatment of all current and prospective employees and does not condone discrimination on the basis of age, disability, sex, sexual orientation, pregnancy or maternity, race or ethnicity, religion or belief, gender identity or marriage and civil partnership. We aspire to have a diverse and inclusive workplace and strongly encourage suitably qualified applicants from a wide range of backgrounds to apply.

At The Cadillac Formula 1 ® Team, all Team Members are expected to actively support and uphold our policies and procedures, including those focused on Environmental responsibility, Sustainability initiatives, Inclusion and Health and Safety practices.

Please note that additional security checks may be required as part of the recruitment process. This may include a background check covering a minimum of the past five years and a criminal record check.

• Developing and implementing complex quantitative models for derivative pricing, risk management, and portfolio optimization.
• Conducting rigorous model validation and back-testing to ensure accuracy and reliability.
• Programming in languages such as Python, C++, or R for model development and implementation.
• Collaborating with traders, risk managers, and portfolio managers to understand their needs and provide quantitative solutions.
• Contributing to the research and development of new pricing methodologies and risk measures.
• Monitoring and analyzing market data to identify trends and opportunities.
• Ensuring compliance with regulatory requirements and internal policies.
• Documenting model specifications, methodologies, and assumptions thoroughly.
• Communicating complex quantitative concepts clearly to both technical and non-technical stakeholders.
• Mentoring junior quantitative analysts and sharing knowledge within the team.
• Assisting in the implementation of new trading systems and risk platforms.
• Performing ad-hoc analysis and research as required by senior management.

• Lead the design and execution of biophysical experiments to characterize molecular interactions and mechanisms.
• Develop, validate, and implement advanced computational models for molecular simulations and drug discovery.
• Analyze and interpret large datasets from both experimental and computational studies.
• Collaborate with cross-functional teams including computational chemists, structural biologists, and pharmacologists.
• Mentor junior researchers and contribute to the overall scientific strategy of the R&D department.
• Present findings at international conferences and contribute to high-impact publications.
• Ensure all research activities are conducted adhering to the highest ethical and scientific standards.
• Manage project timelines and resources effectively to meet research objectives.
• Stay abreast of the latest advancements in biophysics, computational biology, and drug discovery.
• Contribute to the development of new research methodologies and technologies.

• PhD in Biophysics, Biochemistry, Computational Chemistry, or a related field.
• A minimum of 5 years of post-doctoral or industry research experience in biophysics and computational modeling.
• Proven expertise in techniques such as Surface Plasmon Resonance (SPR), Isothermal Titration Calorimetry (ITC), Circular Dichroism (CD), and Small-Angle X-ray Scattering (SAXS).
• Strong proficiency in molecular modeling software (e.g., AMBER, GROMACS, CHARMM) and data analysis tools.
• Experience with programming languages like Python or R for data analysis and workflow automation.
• Excellent understanding of protein structure, function, and dynamics.
• Demonstrated ability to lead research projects and mentor team members.
• Exceptional analytical and problem-solving skills.
• Outstanding written and verbal communication skills, with a track record of scientific publications.
• The ability to thrive in a fast-paced, innovative, and collaborative remote working environment.

• Develop, implement, and validate sophisticated quantitative models for financial markets.
• Design and implement algorithms for pricing, risk management, and trading strategies.
• Utilize statistical modeling, machine learning, and econometrics to analyze financial data.
• Perform rigorous back-testing, stress-testing, and sensitivity analysis of models.
• Collaborate with business stakeholders to understand requirements and deliver data-driven solutions.
• Communicate complex quantitative findings to technical and non-technical audiences.
• Ensure model compliance with regulatory requirements and internal policies.
• Contribute to the continuous improvement of quantitative methodologies and infrastructure.

• Master's or PhD in Finance, Mathematics, Statistics, Physics, Computer Science, or a related quantitative field.
• Minimum of 5 years of relevant experience in quantitative analysis within the financial services industry.
• Proven expertise in financial modeling, risk management, and/or algorithmic trading.
• Proficiency in programming languages such as Python, R, C++, or MATLAB.
• Strong knowledge of financial markets, derivatives, and econometrics.
• Excellent analytical, problem-solving, and critical thinking skills.
• Effective communication and presentation skills.
• Ability to work effectively in a hybrid team environment.

• Developing and validating 3D geological and reservoir models using industry-standard software.
• Performing reservoir simulations to predict performance and evaluate development strategies.
• Conducting static and dynamic data integration for model building.
• Analyzing production data and history matching simulation models.
• Estimating reserves and forecasting future production.
• Evaluating the impact of different recovery methods and technologies.
• Collaborating with geoscientists and other disciplines to refine models.
• Communicating complex technical information effectively to stakeholders.
• Mentoring junior engineers and contributing to technical best practices.

• Master's or PhD in Petroleum Engineering, Geology, Geophysics, or a related discipline.
• A minimum of 8 years of experience in petroleum engineering, with a strong focus on subsurface modeling and simulation.
• Expertise in reservoir simulation software (e.g., ECLIPSE, CMG, INTERSECT) and geological modeling software (e.g., Petrel, GeoMod).
• Proficiency in programming languages (e.g., Python, MATLAB) for data analysis and scripting is advantageous.
• Strong analytical and problem-solving skills, with the ability to interpret complex subsurface data.
• Excellent communication, collaboration, and presentation skills.
• Proven ability to work independently and manage projects effectively in a remote setting.

• Designing, developing, and testing quantitative models for financial instruments such as equities, fixed income, currencies, and commodities.
• Implementing pricing models, risk management systems, and algorithmic trading strategies.
• Performing rigorous backtesting and validation of models to ensure accuracy and reliability.
• Analyzing market data and identifying trends to inform model improvements and new strategy development.
• Collaborating with IT and development teams to integrate models into production environments.
• Contributing to the documentation of model methodologies, assumptions, and limitations.
• Staying current with academic research and industry best practices in quantitative finance and machine learning.
• Presenting complex analytical results clearly and concisely to both technical and non-technical stakeholders.
• Ensuring models adhere to internal risk policies and external regulatory requirements.
• Monitoring model performance and conducting periodic reviews and recalibrations.