Data Scientist

Repost of JP 14031 and JP 14291 Please no candidates from that last posting. PRESCREENING QUESTIONS TO BE ON TOP OF RESUME: 1. For an internal flow, how do you determine if the flow is laminar or turbulent? 2. What is your experience with FEM commercial solvers, and which ones have you used? 3. Can you explain the difference between explicit and implicit finite element methods (FEM)? 4. What is your experience with Python, and which Python libraries have you used most frequently for data analysis, modeling, or simulation? 5. Are you familiar with GitLab? If so, can you describe some of the typical operations you have performed with it? 6. Are you familiar with Monte Carlo analysis? If yes, how do you ensure convergence in a Monte Carlo model? 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 *** 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)