Principal Data Scientist

at  NeurOptics

NeurOptics Inc. is the leader in the science of pupillometry. Driven by a passion to help clinicians improve patient outcomes, NeurOptics develops and markets innovative technologies facilitating improved medical decision making and enabling clinical research in critical care medicine, neurology, neurosurgery, emergency medicine, and research. Headquartered in Irvine, California, NeurOptics, Inc. is represented in over 40 countries worldwide.
At NeurOptics, we are passionate patient advocates. For us, helping clinicians provide improved neurological patient outcomes is our life’s work.
Job Overview
We are seeking a Principal Data Scientist with expertise in machine learning, data analytics, and clinical data management to drive the development and integration of cutting-edge tools for data visualization, exploration, and analysis. This role will focus on leveraging advanced analytics and AI techniques to derive actionable insights from complex clinical data, with an emphasis on building strong analytical capabilities across the department. As a key member of the team, you will collaborate closely with department leaders and stakeholders to support scientific hypothesis testing, experimental design, and strategic decision-making in the clinical domain.
The ideal candidate will have deep experience in high-dimensional data analysis, clinical data (especially from clinical trials), and AI-driven feature development. You should be passionate about designing and implementing solutions that help drive a deep understanding of clinical outcomes and patient profiles, while innovating new methods for data visualization and analysis.

QUALIFICATIONS

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• Education:

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• Bachelor’s degree (Master’s or PhD preferred) in Data Science, Bioinformatics, Computer Science, Statistics, Mathematics, or related field.
• Minimum 5 years of experience in a data science, bioinformatics, or computational role within a biotech/pharma setting.
• Technical Skills:
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• Programming: Expert proficiency in Python, R, or similar languages; experience with R/Shiny, SQL, and Tableau for data analysis, visualization, and reporting.
• Machine Learning & AI: Strong background in machine learning, statistical modeling, clustering, classification, and predictive analytics. Experience with deep learning and Generative AI (GenAI) techniques is a plus.
• Data Analysis: Extensive experience in multivariate data analysis and working with large, high-dimensional datasets in the life sciences or clinical research context.
• Experience:
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• Proven experience developing, evaluating, and deploying machine learning models in a real-world clinical setting.
• Familiarity with handling clinical trial data, patient datasets, and investigator-initiated trials.
• Strong experience in experimental design, hypothesis testing, and driving insights from data to influence strategic decisions.
• Tools & Technologies:
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• Expertise in Python, SQL, R, and data analysis libraries (e.g., Pandas, NumPy, SciPy).
• Experience with Tableau or similar data visualization tools.
• Knowledge of data processing, model serving, and end-to-end machine learning pipeline development.
• Soft Skills:
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• Communication: Exceptional ability to communicate complex data findings to both technical and non-technical audiences. Ability to distill data insights into clear narratives and strategic recommendations.
• Team Player: Collaborative mindset, with strong interpersonal skills to work effectively within cross-functional teams.
• Problem Solving & Creativity: Strong analytical thinking, creative problem-solving, and the ability to innovate new approaches to complex data challenges.

PREFERRED SKILLS

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• Experience in biotech/pharma and working with clinical data.

• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and cloud platforms for model deployment (e.g., AWS, Google Cloud, Azure).
• Advanced statistical knowledge and experience with statistical packages (e.g., SPSS, SAS) is a plus.

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• Tool Development & Integration: Build and integrate tools for data exploration, visualization, and analysis, transforming complex data into clear insights related to patient profiles, clinical outcomes, and mechanisms of action.

• Data Management & Analysis: Manage and analyze high-dimensional clinical data from trials, patient use, and investigator-initiated studies, ensuring the successful delivery of strategic insights through advanced analytics.
• Collaboration & Strategic Input: Partner with cross-functional teams and key stakeholders to understand data requirements, deliver analytical insights, and provide recommendations that drive scientific understanding and business strategy.
• Machine Learning & AI Model Development: Design, implement, and productionize machine learning models, leveraging both classical machine learning and Generative AI (GenAI) techniques to uncover patterns, predict outcomes, and support hypothesis testing.
• Modeling & Experimentation: Design experiments and conduct analysis to answer key clinical and scientific questions, driving new insights from data and optimizing experimental design.
• Full Lifecycle Ownership: Own the complete lifecycle of model development, from ideation and data exploration to model deployment, validation, monitoring, and continuous improvement in production environments.
• Data Visualization & Dashboards: Develop interactive dashboards and other data visualization tools that enable both technical and non-technical stakeholders to understand and interpret complex data trends.
• Communication & Reporting: Effectively translate complex findings and data science outputs into clear, actionable insights for diverse audiences (scientific, technical, and business). Provide detailed reports and presentations summarizing key findings and recommendations.
• KPI Development & Monitoring: Develop and track key performance indicators (KPIs) to evaluate the success of models, analyses, and clinical outcomes.
• Innovation: Continuously explore new opportunities for applying advanced analytics techniques, including machine learning, predictive modeling, and AI, to drive innovation in clinical research.
• Statistical Support: Provide comprehensive statistical support for clinical research projects, ensuring that robust statistical methodologies are applied throughout the study lifecycle. Key responsibilities include: study design & power analysis, statistical methodology & analysis, comparative analysis & reporting, regulatory compliance & documentation, collaboration & guidance and continuous improvement.