Partner with cross-functional teams (e.g., disease subject-matter-experts, data scientists, statisticians, precision measures leads and clinicians) on (a) study design, development and validation of algorithms and analysis pipelines for digital health data; (b) application and development of novel modeling techniques to combine digital health data with imaging, blood-based biomarkers and real world data to enable precision neuroscience.
Partner with cross-functional teams to support strategic roadmaps that integrate digital health solutions throughout the patient journey, ensuring clinical relevance and patient-centered outcomes.
Contribute to evidence packages to support regulatory qualifications of digital endpoints and tools.
Partner with regulatory and medical affairs to engage health authorities and payers on the acceptance of digital health approaches.
Ensure validation and compliance with evolving global regulatory and data privacy standards.
Partner with cross functional team to integrate digital health technologies into trial and ensure high standard in data capture, integration, and interoperability of digital solutions.
Represent the organization in external consortia, conferences, and collaborations to influence digital health standards in neuroscience.

Requirements

Advanced degree (PhD, MD or equivalent) in neuroscience / quantitative sciences such as biomedical engineering, data science, biostatistics or a related field
6 years of experience in regulated environment such as pharmaceutical, biotech, or medical device
Experience in clinical development, digital health or outcome assessments, with demonstrated expertise in neuroscience preferred
Strong track record in developing and validating digital health tools, clinical endpoints, or biomarkers, ideally with regulatory interactions and familiarity with industry leading validation frameworks and relevant regulatory guidelines
Familiarity with data standards, privacy regulations, and regulatory qualification pathways for digital measures
Hands on experience in advanced analytical methodologies, including machine learning and statistical modeling of digital health data such as speech and sensor data and/or neuroimaging
Knowledge in multimodal data integration and modeling of disease progression trajectories is a plus
Expert-level proficiency in Python and/or R with experience in commonly used data science and machine learning libraries
Prior experience working with cloud-base system and/or open platforms for data science preferred
Excellent communication and collaboration skills
Track records of scientific contributions (presentations and publications) in this field is required.

Benefits

medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance
consolidated retirement plan (pension)
savings plan (401(k))
Vacation – up to 120 hours per calendar year
Sick time - up to 40 hours per calendar year
Holiday pay, including Floating Holidays – up to 13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

advanced analytical methodologiesmachine learningstatistical modelingdigital health toolsclinical endpointsbiomarkersdata integrationPythonRneuroimaging

Soft Skills

communicationcollaboration

Certifications

PhDMD