Overview
On Site
Full Time
Skills
Mechanical Engineering
Biomedicine
Chemical Engineering
GMP
Adaptability
Computer Science
Electrical Engineering
Computer Engineering
Lifecycle Management
Decision-making
Physics
Analytics
Process Improvement
Visualization
Analytical Skill
Python
MATLAB
JMP
Minitab
Heat Transfer
Ansys
LS-DYNA
Abaqus
COMSOL
Monte Carlo Method
GitLab
Version Control
Collaboration
Project Management
Communication
Presentations
Technical Writing
As-is Process
Process Control
Medical Devices
Machine Learning (ML)
Leadership
Algorithms
Management
Science
Modeling
Data Analysis
Job Details
Hybrid at Thousand Oaks. Expectation is 3 days onsite per week.
The ideal candidate is a Data Scientist with a strong engineering background, preferably in Mechanical, Biomedical, or Chemical Engineering, with proven experience in simulation, modeling, and data analysis within a GMP-regulated environment. They are proficient in Python, MATLAB, and Minitab, and possess a deep understanding of engineering principles applied to physical systems. The candidate holds either a PhD with no required industry experience, a Master's degree with at least 2 years of experience, or a Bachelor's degree with a minimum of 4 years of experience. They are highly analytical, collaborative, and adaptable, with the ability to translate experimental data into actionable insights. Candidates with degrees in Computer Science, Electrical Engineering, or Computer Engineering, or those with purely software-focused backgrounds, are not a fit for this role.
The Digital Data Scientist will support the Combination Product Operations organization by improving the way Client manages and utilizes data to enhance data analysis and decision making within the organization. We are seeking a highly motivated individual who will be primarily responsible for development and lifecycle management of digital modeling assets and analyzing scientific and combination product performance data. This individual will leverage in-silico and data-driven modeling to evaluate potential opportunities that enable changes in business and operation performance.
The ideal candidate enjoys tackling challenges and excels at enabling insights for decision making using data-driven and physics-based modeling.
This may include, but is not limited to, the following:
Applying engineering principles to develop in-silico models for combination products
Developing, enhancing, automating, and managing analytics and data-driven models
Performing ad-hoc analysis and supporting special projects; Providing input to management for trend and failure investigation process improvements
Demonstrating modeling and visualization approaches as part of proof-of-concept projects
Transforming ambiguous business and technical questions into measurable and impactful projects
Demonstrating critical and analytical thinking skills to explore new opportunities in in-silico and data-driven models for combination products.
Skills:
Experience with programming in Python, MATLAB, JMP, and/or Minitab for engineering purposes
Experience with model simulation and analysis (MS&A) techniques for structural, fluidic, and heat transfer problems using commercial software such as ANSYS, LS-Dyna, ABAQUS, COMSOL
Experience with mathematical/first principles modeling, numerical techniques, and uncertainty quantification such as Monte Carlo simulations
Familiar with utilizing GitLab for version control, code collaboration, and project management
Data analysis expertise and statistical or mechanistic modeling experience
Experience in deriving technical recommendations and specifications from the analysis of measured data
Strong communication, presentation, and technical documentation skills are a plus, as is knowledge of process controls
Understanding business needs and developing Client yet practical solutions to meet those needs
Experience with combination products and device regulatory requirements and medical device development and engineering
Preferred Traits:
Passion for proactively identifying opportunities through creative modeling and data analysis
Transform ambiguous business and technical questions into measurable and impactful projects
Partner with multi-discipline digital teams (data analysts, data engineers, data scientists, and business product owners) to advance data analytics tools/features (such as predictive/ prescriptive algorithms and machine learning)
Ability to deliver work and provide positive leadership in a fast-paced, multi-project team-oriented environment
Intellectual curiosity with ability to learn new concepts/frameworks, algorithms and technology rapidly as needs arise
Ability to manage multiple competing priorities simultaneously
Ability to work in highly collaborative, cross-functional environments
Basic Qualifications:
Bachelor's degree in Engineering plus 5 years of simulation, modeling, and data analysis experience
Or
Master's degree in Science or Engineering plus 2 years of simulation, modeling, and data analysis experience
Or
Ph.D. in Science or Engineering (simulation, modeling, and data analysis)
The ideal candidate is a Data Scientist with a strong engineering background, preferably in Mechanical, Biomedical, or Chemical Engineering, with proven experience in simulation, modeling, and data analysis within a GMP-regulated environment. They are proficient in Python, MATLAB, and Minitab, and possess a deep understanding of engineering principles applied to physical systems. The candidate holds either a PhD with no required industry experience, a Master's degree with at least 2 years of experience, or a Bachelor's degree with a minimum of 4 years of experience. They are highly analytical, collaborative, and adaptable, with the ability to translate experimental data into actionable insights. Candidates with degrees in Computer Science, Electrical Engineering, or Computer Engineering, or those with purely software-focused backgrounds, are not a fit for this role.
The Digital Data Scientist will support the Combination Product Operations organization by improving the way Client manages and utilizes data to enhance data analysis and decision making within the organization. We are seeking a highly motivated individual who will be primarily responsible for development and lifecycle management of digital modeling assets and analyzing scientific and combination product performance data. This individual will leverage in-silico and data-driven modeling to evaluate potential opportunities that enable changes in business and operation performance.
The ideal candidate enjoys tackling challenges and excels at enabling insights for decision making using data-driven and physics-based modeling.
This may include, but is not limited to, the following:
Applying engineering principles to develop in-silico models for combination products
Developing, enhancing, automating, and managing analytics and data-driven models
Performing ad-hoc analysis and supporting special projects; Providing input to management for trend and failure investigation process improvements
Demonstrating modeling and visualization approaches as part of proof-of-concept projects
Transforming ambiguous business and technical questions into measurable and impactful projects
Demonstrating critical and analytical thinking skills to explore new opportunities in in-silico and data-driven models for combination products.
Skills:
Experience with programming in Python, MATLAB, JMP, and/or Minitab for engineering purposes
Experience with model simulation and analysis (MS&A) techniques for structural, fluidic, and heat transfer problems using commercial software such as ANSYS, LS-Dyna, ABAQUS, COMSOL
Experience with mathematical/first principles modeling, numerical techniques, and uncertainty quantification such as Monte Carlo simulations
Familiar with utilizing GitLab for version control, code collaboration, and project management
Data analysis expertise and statistical or mechanistic modeling experience
Experience in deriving technical recommendations and specifications from the analysis of measured data
Strong communication, presentation, and technical documentation skills are a plus, as is knowledge of process controls
Understanding business needs and developing Client yet practical solutions to meet those needs
Experience with combination products and device regulatory requirements and medical device development and engineering
Preferred Traits:
Passion for proactively identifying opportunities through creative modeling and data analysis
Transform ambiguous business and technical questions into measurable and impactful projects
Partner with multi-discipline digital teams (data analysts, data engineers, data scientists, and business product owners) to advance data analytics tools/features (such as predictive/ prescriptive algorithms and machine learning)
Ability to deliver work and provide positive leadership in a fast-paced, multi-project team-oriented environment
Intellectual curiosity with ability to learn new concepts/frameworks, algorithms and technology rapidly as needs arise
Ability to manage multiple competing priorities simultaneously
Ability to work in highly collaborative, cross-functional environments
Basic Qualifications:
Bachelor's degree in Engineering plus 5 years of simulation, modeling, and data analysis experience
Or
Master's degree in Science or Engineering plus 2 years of simulation, modeling, and data analysis experience
Or
Ph.D. in Science or Engineering (simulation, modeling, and data analysis)
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