LifeQ is seeking a dynamic and experienced candidate for the role of Computational Biologist.
LifeQ provides a fast-paced and dynamic work environment with access to industry-leading technology and collaboration with influential international corporations. The LifeQ team is fully remote-capable during the COVID-19 pandemic and will resume working in the Alpharetta, GA office when it is safe to do so.
The ideal candidate will be someone with an enthusiasm for first-principle thinking, great interpersonal skills, a willingness to learn and adapt to new challenges, and a drive for developing innovative products that will launch the company forward. Long term success in this role will include the ability to guide technical projects at a high level and to ensure they are completed to specification and on-time.
The Computational Biologist will work in interdisciplinary teams on projects incorporating a wide range of models and biologically inspired computational methods, using existing data and data forthcoming from pilot initiatives. These projects will involve extracting relevant physiological, behavioral, and environmental information from technology including smart watches and other gold standard monitoring equipment, and producing models from extracted features in order to predict health and wellness outcomes (e.g. stress or disease risk) or characterize behavioral choices (e.g. exercise).
For some projects, the applicant may be tasked with reviewing and upgrading existing models by adding new data, employing new algorithms, or applying new learnings made in the relevant field(s). Research and development work must be conducted following a well-designed scientific process with the final goal of implementing the solution according to a product specification.
The Computational Biologist will report to the Science Team leads and the company’s Chief Scientist.
- Conduct literature-based research to develop a qualitative and quantitative understanding of the latest developments in public health studies and published models relating to research objectives.
- Develop advanced integrative mathematical models in support of research objectives.
- Follow proper scientific methodology to validate algorithms/models, both new and old (e.g. measuring quantitative performance statistics, testing assumptions and limitations, assessing bias and variance of models, etc.).
- Clearly capture and document learnings made during the research and development process, both for internal knowledge sharing and in public-facing documents (e.g. white papers).
- Contribute to the productization of the models, i.e. envision how the model outputs can be packaged and presented to users, anticipate and resolve possible friction points from a user’s perspective, and assist with user education.
- Produce relevant technical specification documents for models.
- Participate in study design for relevant projects.
- When applicable, contribute to the development of intellectual property filings.
- Create and maintain any code produced for models, algorithms, or data analysis on internal repositories.
- Follow best practices relating to collaborative coding and version control.
- Continuously improve relevant skills to meet the evolving demands of novel projects.
- Build on multi-disciplinary knowledge to enable teamwork and collaboration with various teams within the organization.
Candidates with a M.S. or Doctorate level education, and/or at least 3 years experience in relevant technical roles, are invited to apply if they hold any of the following qualifications:
- Experience/background in bioengineering, biological modeling and/or computational systems biology.
- Experience/background in life and/or medical science(s).
- Experience/background in physics and/or applied mathematics.
General programming experience is required, including at least a basic comfort in Python. Additional experience/skills which will carry extra weight in the application include:
- Experience/skill in data science related work (feature engineering, modeling, predictive analytics, etc.), particularly in cloud environments.
- Experience/skill in applying machine learning techniques (e.g. Neural Networks, Support Vector Machines, Random Forests).
- Experience/skill in ordinary differential equation-based modelling.
- Experience/skill in analyzing time series data, and/or digital signal processing (frequency domain analysis).
- Experience in using software version control methods (e.g. Git repositories).
- Experience/skill in using serverless computing platforms e.g. AWS Lambda.
- Experience/skill in connecting to external API data resources, such as those hosting digital health data.
- Experience/skill in biometrics and the understanding of concepts arising in public health studies.
- Experience with regulation and/or regulatory bodies e.g. the FDA.