Machine Learning Scientist

The Machine Learning Scientist will support a diverse set of initiatives to improve patient selection, treatment and follow-up via the development of novel machine learning & AI algorithms.

You will partner with internal and external stakeholders to develop insights and models to advance discoveries in mental health care.

Roles and responsibilities

• Provide expertise company-wide on a variety of Machine Learning and AI topics in order to improve the patient journey and provide mission-critical applications as COMPASS grows
• Develop algorithms & methodologies that incorporate digital and traditional biomarkers (voice, vision, smart device, etc.) in order to improve the treatment process
• Work collaboratively with engineering (back-end/front-end/UI+UX/ and others) to deploy these algorithms in a highly scalable and distributed infrastructure to help patients in real time
• Communicate insights with stakeholders with the ability to extract therapeutic intelligence by leveraging external and internal data sources
• Work closely with stakeholders to identify quantitative and technological needs for various COMPASS projects

Detailed responsibilities/accountabilities

• The position reports to the VP of Digital Health Research

Profile

• Substantial experience in the field either through industry and/or academia. Biotech & start-up experience a strong plus (Degree in: Statistics, Computer Science, Neuroscience, Engineering, Mathematics, Physics or related)
• Strong analytical, project management, written and verbal communication skills
• Proficient in Python (scikit-learn, numpy, pandas, matplotlib, scipy) and experience with Tensorflow/Keras and/or Pytorch
• Deep experience with at least one of the following: Computer Vision, Natural Language Processing, time-series signal processing, audio signal processing.
• Understanding of at least 2-3 common machine learning & AI architectures (convolutional neural nets, reinforcement learning, attention & transformer-based networks, graph neural nets, generative adversarial networks, etc.)
• Comfortable with Linux and the AWS cloud environment
• Ability to operate independently and in cross-functional teams in an entrepreneurial and rapid growth-oriented environment
• Strong alignment with core company mission and values; Compassionate, Bold, Rigorous, and Inclusive
• Ability to work in the UK or US

Additional qualifications – preferred

• A PhD in a computational field (Degree in Statistics, Computer Science, Neuroscience, Engineering, Mathematics, Physics or related)
• Industry experience in a machine learning or AI role within a digital health organisation, pharma, or biotech
• Experience in R (tidyr, dplyr, ggplot2, Rstudio)
• Background in EEG, fMRI, and other brain measurement technologies a strong plus
• Familiar with R Shiny and/or other app development technologies such as Django/Angular/Clojure

How to apply

If interested, please send your CV and statement of interest to hiring@compasspathways.com

Note that we are not able to sponsor employment visas at this time, and therefore can only accept applications from people who have employment rights in the US or UK.

Equal opportunities

US applicants

COMPASS Pathways is proud to be an equal opportunity employer. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, and related medical conditions), ethnicity, age, disability, sexual orientation, gender identity, gender expression, military service, genetic information, familial or marital status, or any other status, category, or characteristic protected by applicable law.

UK applicants

We are proud of our commitment to diversity and equality (pursuant to the Equality Act 2010). We do not discriminate based upon race, religion or belief, colour, nationality, ethnic or national origin, gender, pregnancy or maternity, marital or civil partner status, sexual orientation, gender reassignment, age or disability.