Head of Science (Algorithms and Causal Health Intelligence)

About the role:Luna builds wearables that turn raw body signals into things people can act on. We are buildingLifeOS, the layer that connects what someone does with how their body responds, andseparates real cause and effect from coincidence. We are hiring a senior scientist to own thatlayer and to set the scientific and algorithmic standard across every product we ship.This is a hands-on leadership role. You will build, validate and ship, and lead a small team ofscientists and algorithm engineers as it grows.What you will do: Own the science of LifeOS: the causal and correlational engine that turns wearable,behavioural and context data into recommendations that measurably move a healthoutcome. Design and run causal inference methods for noisy, observational, individual-level (N-of-1)data, and know where the data cannot support a claim. Lead algorithm development across the company: heart rate, HRV, sleep, activity, recoveryand derived scores from PPG and motion signals. Set one validation standard: reference-based accuracy testing (ECG, PSG, lab), benchmarkdatasets, release gates and regression tracking, so accuracy is measured and tracked, notargued. Design and run validation studies and clinical protocols, including ethics approvals, andpublish where it builds credibility. Work with firmware, app, data and product teams so algorithms move from research toproduction reliably. Define how we test whether an actionable works: experiments, interventions and outcomemeasures. Track regulatory boundaries (wellness versus medical claims) and shape what we can sayabout our features. Hire, mentor and raise the bar for the science and algorithms team.What we are looking for: PhD in biomedical signal processing, computational health, causal machine learning,biostatistics, or a closely related field, from a strong research university in India or abroad.An MS with deep industry track record will be considered. 5 to 8 years of experience after the PhD, in industry or in an applied lab with real productoutput. Demonstrated depth in causal inference on observational data: DAGs, instrumentalvariables, g-methods, Bayesian and time-series causal models, or equivalent. Shipped algorithms on wearable or physiological signals (PPG, ECG, accelerometry, sleep oractivity), including owning accuracy against a reference. Strong study design and statistics. Track record of validation work, with publications ortechnical reports. Fluency in Python and the scientific stack, and comfort reading and reviewing productioncode. Experience with ML in production. Evidence of leading people or technical direction, with a willingness to stay hands-on. Clear communication of uncertainty to engineers, product leaders and non-technicalstakeholders.Strong pluses: Behaviour change or personalised intervention research. Digital biomarkers or clinical-grade validation experience. Experience with regulatory pathways for health software and devices. Experience at a wearables, digital health or medical device company.Who this is not for:This role is not a pure research position and not a pure deep learning role. We need someonewho is equally serious about causal reasoning, signal quality and shipping.What success looks like First 90 days: audit current algorithms and accuracy, agree a single validation frameworkand release gates, and publish a science roadmap. First 6 months: measurable accuracy improvements on core metrics, and a first causal-insight capability running in production. First 12 months: LifeOS recommendations with evidence of impact on user outcomes, and ateam that runs on shared standards.