About Multiscale At Multiscale Technologies, we believe the future is driven by innovative materials, but the traditional approaches to discovering and manufacturing them are outdated and unsustainable.
That's why we've developed cutting-edge technologies to revolutionize this process.
By bringing together world-class materials scientists, mechanical engineers, data scientists, and computer engineers, we're designing next-generation materials and optimizing manufacturing processes to meet the demands of a more sustainable future.
As a Series-A funded startup, Multiscale Technologies offers you an opportunity to make a direct impact on a growing company that's accelerating the world's shift toward advanced materials and sustainable solutions.
If you're ready to push the boundaries of technology and collaborate on game-changing advancements, join us in shaping the future.
Role Overview We are seeking a Data Scientist with experience in solving real-world experimental optimization problems, with a focus on leveraging data to improve workflows and outcomes.
The ideal candidate will work on linking experimental input parameters to measured outputs, building predictive models, and contributing to the development of autonomous workflows in materials informatics.
Key Responsibilities Data Analysis and Modeling: Develop predictive models for linking experimental input parameters with output properties (e.g., structure-property relationships).Analyze multi-modal experimental data (e.g., imaging, spectroscopy, or performance metrics) to extract key features.Build models to optimize experimental conditions or process parameters based on desired material outcomes.Experimental Feedback and Optimization: Collaborate on the design of feedback loops to adjust input parameters dynamically based on measurement results.Develop algorithms for experimental design (e.g., Bayesian optimization, active learning) to minimize the number of experiments required.Toolsets and Libraries: Work with machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow) to implement predictive and optimization models.Utilize tools specific to materials informatics, such as materials property databases or feature extraction libraries.Collaboration and Reporting: Partner with engineers, data engineers, and domain experts to align models with physical principles.Present insights and deliver actionable recommendations to improve experimental workflows.Qualifications Education: Master's or Ph.D. in Data Science, Materials Science, Engineering, Physics, or a related field.Experience:3+ years of experience in data-driven optimization or experimental analytics.Demonstrated ability to solve structure-property or process-property relationship problems.Technical Skills:Proficiency in Python or R for data analysis and machine learning.Familiarity with tools for optimization (e.g., Bayesian optimization, reinforcement learning).Knowledge of experimental workflows or systems generating real-world data.Soft Skills:Excellent problem-solving and collaboration skills.Strong communication skills to present complex data-driven insights.Preferred Skills Experience with multi-modal data analysis (e.g., combining imaging and spectroscopy data).Familiarity with experimental design techniques to reduce experimental workload.Exposure to tools and workflows in materials informatics or related fields.At Multiscale, we are committed to fostering an inclusive and diverse workplace where everyone is respected and valued.
We believe in providing equal employment opportunities to all individuals, regardless of race, ethnicity, gender, sexual orientation, disability, religion, or background.
Our goal is to create an environment where diverse perspectives drive innovation and success, and all employees have the opportunity to thrive and grow.
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