Head Of Science (Algorithms)

About the role:

• Luna builds wearables that turn raw body signals into things people can act on. We are building
• LifeOS, the layer that connects what someone does with how their body responds, and
• separates real cause and effect from coincidence. We are hiring a senior scientist to own that
• layer 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 of
• scientists 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 health
• outcome.
• 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, recovery
• and derived scores from PPG and motion signals.
• Set one validation standard: reference-based accuracy testing (ECG, PSG, lab), benchmark
• datasets, release gates and regression tracking, so accuracy is measured and tracked, not
• argued.
• Design and run validation studies and clinical protocols, including ethics approvals, and
• publish where it builds credibility.
• Work with firmware, app, data and product teams so algorithms move from research to
• production reliably.
• Define how we test whether an actionable works: experiments, interventions and outcome
• measures.
• Track regulatory boundaries (wellness versus medical claims) and shape what we can say
• about 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 product

output.

• Demonstrated depth in causal inference on observational data: DAGs, instrumental
• variables, g-methods, Bayesian and time-series causal models, or equivalent.
• Shipped algorithms on wearable or physiological signals (PPG, ECG, accelerometry, sleep or
• activity), including owning accuracy against a reference.
• Strong study design and statistics. Track record of validation work, with publications or
• technical reports.
• Fluency in Python and the scientific stack, and comfort reading and reviewing production
• code. 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-technical
• stakeholders.

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 someone

who 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 framework
• and 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 a
• team that runs on shared standards