Neuro Data Scientist - Los Angeles - Kandu, Inc.

Description

Kandu, Inc. is pioneering an integrated approach to stroke recovery by combining FDA-cleared brain-computer interface technology with personalized telehealth services. Our IpsiHand device is durable medical equipment that enables chronic stroke survivors to regain upper extremity function with daily home use. Combining this advanced technology with the support of expert clinicians offers a comprehensive path to recovery–helping survivors improve mobility, independence, and quality of life.

As a Neuro Data Scientist, you will serve as a scientific and analytics leader for the organization, shaping evidence generation and research strategy in partnership with Clinical, Product, and R&D leadership. This role offers the opportunity to determine who benefits from our neurorehabilitation devices, quantify clinical and economic impact, and drive future product enhancements through rigorous, data-driven analysis in the stroke rehabilitation space.

You will work closely with world-class teams and leading clinical experts to ensure our solutions are grounded in real-world rehabilitation evidence and supported by compelling, decision-grade evidence for patients, providers, and payers.

What You'll Do

• Analyze manufacturer device datasets (usage, adherence, device performance and utilization measures, demographics, and clinical context) using advanced statistical and data science methods—including heterogeneity of treatment effect approaches—to quantify outcomes, identify responder profiles, and determine which patient segments and usage patterns drive benefit in stroke neurorehabilitation.
• Design and execute real‑world evidence analyses, including cohort construction, endpoint definition, risk adjustment, and longitudinal follow‑up; integrate/link device data with external sources (EHR, claims, registries, PROs) when available, using reproducible, QA'd analytic datasets and pipelines.
• Lead HEOR evidence generation to quantify the device's clinical and economic value—analyzing healthcare utilization and costs and partnering with HEOR experts as needed on cost‑effectiveness and/or budget impact models—translating results into payer/provider‑ready evidence and a clear value story.
• Translate findings into peer‑reviewed publications and prioritized product enhancement recommendations; lead scientific writing (protocols, analysis plans, evidence reports, manuscripts) and drive abstract/poster submissions and peer‑review responses.
• Present findings internally and externally, representing the organization at scientific congresses as needed.
• Other duties as defined

What You'll Bring

• Stroke Neurorehabilitation Outcomes Expertise – Demonstrated experience in stroke neurorehabilitation outcomes research, including defining clinically meaningful endpoints and interpreting results in real‑world settings.
• Advanced Analytics & Real-World Evidence Methods – Proven ability to analyze manufacturer device data (usage, adherence, device performance/utilization measures) and execute rigorous RWE studies (cohort construction, risk adjustment, longitudinal analyses), including heterogeneity of treatment effect approaches.
• HEOR Value Demonstration – Experience generating HEOR evidence, including healthcare utilization and cost analyses, and the ability to partner on cost‑effectiveness and/or budget impact models to support payer/provider value narratives.
• Scientific Writing & Publication Leadership – Strong scientific writing capability with first‑author peer‑reviewed publications; experience developing protocols, analysis plans, evidence reports, and driving abstracts/posters/manuscripts through submission and peer review.

Preferred Qualifications:

• 5-10 years of experience as a Scientist, Data Scientist, Biostatistician, Advanced degree preferred (PhD/ScD/DrPH or MS) with significant experience in data science, rehabilitation science, biostatistics, epidemiology, health economics, biomedical engineering, or related field.
• Direct experience in stroke neurorehabilitation (clinical research, outcomes, rehabilitation workflows, or closely related neuro rehab domain).
• Deep expertise in real-world evidence and causal inference using observational healthcare data (e.g., propensity methods, longitudinal modeling, quasi-experimental designs). Demonstrated experience analyzing medical device and/or real‑world healthcare data and communicating results to cross‑functional teams.
• Experience with linked healthcare datasets (device + EHR/claims/registry/PRO) and employing disciplined software development techniques to build scalable, reproducible analytics (e.g. significant experience with SQL, Git, QA/documentation).
• Strong applied statistics skills, including regression modeling, longitudinal analyses, and appropriate handling of bias/confounding in observational data.
• Experience with HEOR (utilization/cost analyses and/or economic modeling) and translating evidence into a clear value story.
• Strong external scientific presence, including first‑author publications, conference presentations, and comfort representing the company with clinical/scientific stakeholders.
• Proven product impact, translating evidence into roadmap decisions, measurable success criteria, and post‑launch performance monitoring in med device/digital health.
• Employment eligibility to work in the United States is required. The company does not sponsor employment visas for this position at this time.

What We Offer

• Competitive Compensation ($180,000+/annually DOE+ Stock Options)
• Insurance (Medical/Dental/Vision)
• 401(k) with company
• Unlimited PTO & Holidays
• Life Insurance, LTD and STD

Please note that the salary information is a general guidance only. Kandu, Inc. considers factors such as scope and responsibilities of the position, candidate's work experience, education/training, key skills and internal parity, as well as location, market and business considerations when extending an offer.

Kandu, Inc. is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.