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Day 15
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London, England, United Kingdom
Live, first seen by us 15 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Set standards for preprocessing, modelling and validation of spectral data
Decide when models are scientifically ready, checking robustness across batches, sites and instruments
Interpret raw optical signatures from plasma and nanoparticle samples and diagnose artefacts
Design experiments and lab protocols that use scarce clinical samples well
Lead scientific relationships with CROs, contractors and academic groups
Set iLoF's standards for preprocessing, modelling and validation of spectral data
Decide when a model is scientifically ready to move forward: review validation, robustness across batches, sites and instruments, and the risk of overfitting.
Interpret raw optical signatures from plasma and nanoparticle samples; diagnose artefacts such as baseline drift, autofluorescence, scattering and instrument variability.
Design experiments (DoE) and lab protocols so every measurement is information-rich and scarce clinical samples are used well.
Lead the scientific relationship with CROs, contractors and academic groups
Proven track record building predictive models on real instrument-generated data (ideally spectral or optical) where sample sizes were tightly constrained. Experience limited to simulated or standard benchmark datasets is not sufficient.
Experience with blood plasma or other biofluid spectroscopy.
Deep expertise in chemometrics and statistical learning for high-dimensional, correlated data, including rigorous validation and prevention of data leakage.
Experience setting scientific direction for others and managing external scientific partners or contractors.
Must have - PhD in biophotonics, computational physics, chemometrics, applied mathematics or a closely related quantitative field. - One of:
- One of: - Industry or translational route: 5–8+ years after your PhD delivering R&D outcomes in industry, a deep-tech start-up or scale-up, a scientific consultancy or a translational research setting, including leading technical work through to a product, validated method or clinical study; or - Research route: 8+ years of post-PhD research with a strong publication record in relevant fields (spectroscopy, chemometrics, biomedical signal analysis or ML for physical data) and a track record of leading major projects, for example as principal or co-investigator on large funded programmes or multi-partner consortia.
- Industry or translational route: 5–8+ years after your PhD delivering R&D outcomes in industry, a deep-tech start-up or scale-up, a scientific consultancy or a translational research setting, including leading technical work through to a product, validated method or clinical study; or - Research route: 8+ years of post-PhD research with a strong publication record in relevant fields (spectroscopy, chemometrics, biomedical signal analysis or ML for physical data) and a track record of leading major projects, for example as principal or co-investigator on large funded programmes or multi-partner consortia. - Proven track record building predictive models on real instrument-generated data (ideally spectral or optical) where sample sizes were tightly constrained. Experience limited to simulated or standard benchmark datasets is not sufficient.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 15
Similar jobs usually come down between day 7 and day 57.
From 10,552 similar postings that came down. Coming down is not always a hire.
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Who we are looking for You combine the mathematical depth of a machine learning scientist with the physical intuition of a spectroscopist. You are as comfortable reading an optics paper or reviewing Python code as you are walking into the lab to look at the instrument. You make sound decisions with incomplete data, explain your reasoning to people from other disciplines, and want to see your work become a clinical product. Qualifications
- Deep expertise in chemometrics and statistical learning for high-dimensional, correlated data, including rigorous validation and prevention of data leakage. - Strong Python (e.g. NumPy/SciPy, scikit-learn, PyTorch) with an emphasis on transparent, reproducible analysis. - Experience setting scientific direction for others and managing external scientific partners or contractors.
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New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
What you will own
Signal-to-model methodology (about 40%)
Set iLoF's standards for preprocessing, modelling and validation of spectral data in high-dimensional, small-sample settings (p ≫ n, typically N < 200 per cohort).
Choose and develop the right methods: chemometrics and dimensionality reduction (e.g. PLS, PCA, wavelets, regularised and Bayesian models), and, where the data support it, physics-informed, self-supervised, metric-learning and transfer-learning approaches.
Design data augmentation and domain-adaptation strategies, including physically grounded synthetic spectra, that expand training data without introducing bias or leakage.
Decide when a model is scientifically ready to move forward: review validation, robustness across batches, sites and instruments, and the risk of overfitting.
Spectral interpretation and experimental design (about 40%)
Interpret raw optical signatures from plasma and nanoparticle samples; diagnose artefacts such as baseline drift, autofluorescence, scattering and instrument variability.
Be the technical bridge between hardware, data science and biology: check physical variables (integration time, laser stability, detector noise) before algorithmic fixes.
Design experiments (DoE) and lab protocols so every measurement is information-rich and scarce clinical samples are used well.
Mentor scientists and data scientists on the physics of the data and on rigorous experimental practice.
External scientific partners (about 20%)
Lead the scientific relationship with CROs, contractors and academic groups: write technical statements of work, set data and code standards, and review what comes back.
Work peer-to-peer with university principal investigators and keep collaborations aligned with iLoF's timelines.
Make sure data and code shared externally protects iLoF's IP, working with our data protection lead.
Working with the Head of Innovation
You will report to and work directly with iLOF's Head of Innovation as their senior scientific partner: shaping the technology roadmap, testing long-term technology bets, and taking scientific decisions within your remit with a high degree of autonomy. The role is designed to grow into technical executive leadership.
Who we are looking for
You combine the mathematical depth of a machine learning scientist with the physical intuition of a spectroscopist. You are as comfortable reading an optics paper or reviewing Python code as you are walking into the lab to look at the instrument. You make sound decisions with incomplete data, explain your reasoning to people from other disciplines, and want to see your work become a clinical product.
Qualifications
Must have
PhD in biophotonics, computational physics, chemometrics, applied mathematics or a closely related quantitative field.
One of:
Industry or translational route: 5–8+ years after your PhD delivering R&D outcomes in industry, a deep-tech start-up or scale-up, a scientific consultancy or a translational research setting, including leading technical work through to a product, validated method or clinical study; or
Research route: 8+ years of post-PhD research with a strong publication record in relevant fields (spectroscopy, chemometrics, biomedical signal analysis or ML for physical data) and a track record of leading major projects, for example as principal or co-investigator on large funded programmes or multi-partner consortia.
Proven track record building predictive models on real instrument-generated data (ideally spectral or optical) where sample sizes were tightly constrained. Experience limited to simulated or standard benchmark datasets is not sufficient.
Deep expertise in chemometrics and statistical learning for high-dimensional, correlated data, including rigorous validation and prevention of data leakage.
Strong Python (e.g. NumPy/SciPy, scikit-learn, PyTorch) with an emphasis on transparent, reproducible analysis.
Experience setting scientific direction for others and managing external scientific partners or contractors.
Advantageous
Experience with blood plasma or other biofluid spectroscopy.
Physics-informed ML, self-supervised or metric learning, domain adaptation.
Exposure to diagnostics, IVD or regulated product development.
The opportunity
Own the scientific core of a proprietary diagnostic platform, work directly with iLoF's leadership, and see your methods shape a clinical test.
iLOF in a nutshell 💡
As personalized medicine becomes the norm, scientists, patients, and healthcare systems worldwide face massive barriers in providing the right treatment to the right patient.
Recognized by CB Insights and Financial Times as one of the most promising Digital Health companies in the world, iLOF is building an AI-powered platform capable of transforming precision medicine.
Our goal is to become the platform that democratizes access to personalized medicine for millions of people living with complex diseases around the globe.
Thanks to our proprietary optical fingerprint library of disease biomarkers and biological profiles we can offer personalized patient screening and stratification in a faster and more affordable way, saving up to 40% of the cost and 70% of the time while enabling a more human patient experience.
Supported by leading institutions like Microsoft Ventures, Mayfield, and the Oxford University, iLOF is currently focusing on validating its platform technology in one of the biggest challenges of our times: Alzheimer's, while maintaining ongoing verticals on Digestive Cancer, Stroke, and Infectious diseases.
Our Team 🧑🏻💻
We are a mix of scientists, entrepreneurs, and inventors based in London and Porto, with a passion for healthcare!
Recognized by CB Insights as one of the most promising Digital Health companies in the world and by the Financial Times as a Transformational Business in Healthcare, iLOF has been regularly featured in leading outlets like Tech Crunch, Wired, and Sifted and listed as a rising star in AI-Driven Drug Discovery, Health Screening & Data Analytics.
What we value
A Be Kind attitude is the main guideline within iLoF
A hand in the hammer and another free to support your co-workers as a true team player
An honest and egoless attitude towards discussions and critics working in good faith
The braveness to speak your heart in any situation
Through words or actions be always sure to make a difference at any moment!
Be proud to drive your responsibilities to success and help others do the same, embracing ownership!
Perks 😉
An exciting future with a deep-tech company selected by Financial Times and CB Insights as one of the most transformative companies in the world
A high‑paced, flexible environment: adaptable working hours, strong remote work culture
A stimulating atmosphere with room for creativity – fast‑paced, fun, and energetic start‑up with a friendly culture
Weekly happy hours with amazing food and drinks
Free coffee and snacks ☕
The opportunity to work with a world‑class, talented team – and potentially change healthcare as we know it for millions of patients living with complex diseases around the globe 🧑⚕️
We share a passion for being part of a mission that matters, and we are always looking for curious and collaborative people who share our values and want to be part of our journey.
If that sounds like a fit for you, click the apply button and join us.
You’ve read all this way but you’re missing a skill or two? No problem, it’s our job to upskill you to take your career to the next level. What we’re trying to say is, don’t be afraid to apply if you don’t tick all the boxes.
Join iLOF to lead the science that turns optical measurements of blood plasma into reliable, clinically useful biomarkers. Our Optomics® platform combines spectroscopy and AI to detect disease signatures in a blood sample, and we are moving from research towards validated tests.
Our hardest problem sits at the interface between physics and data: extracting real biological signal from noisy, liquid-phase spectra (FTIR, Raman and related techniques) when there are thousands of spectral features but only a few hundred precious patient samples. Solving it well decides whether our models generalise to new patients, sites and instruments.
As Principal Scientific Lead you will own that problem. You will set how iLoF acquires, cleans, models and validates spectral data, act as the senior scientific authority across our physics, data science, biology and hardware teams, and work directly with the Head of Innovation on iLoF's technical direction and longer-term technology bets.
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London, England, United Kingdom
- An exciting future with a deep-tech company selected by Financial Times and CB Insights as one of the most transformative companies in the world - A high‑paced, flexible environment: adaptable working hours, strong remote work culture - A stimulating atmosphere with room for creativity – fast‑paced, fun, and energetic start‑up with a friendly culture
No clear work-authorization passage found. Eligibility is unconfirmed.