HealthTell, Inc is an early stage life sciences company with locations in Chandler, AZ and San Ramon, CA that has developed a cost effective and powerful new technology for the early detection and monitoring of progressive diseases such as cancer. Unlike most conventional diagnostics, this test does not target the diseased cell or its products, but instead measures the body’s responses to the diseased state by way of the repertoire of antibodies that the disease elicits. Because the immune system is the body’s defense line against virtually all diseases, it starts acting at the earliest stages of not only infection, but also chronic diseases such as cancer. HealthTell can precisely and accurately detect the earliest presence of a disease, and monitor its progression over time using its proprietary OneTest approach. Because diseases are most effectively treated at early stages, this approach enables consumers to monitor their own health and seek treatment when it is most advantageous for them.
We see the HealthTell OneTest as an ideal platform for early diagnosis of chronic diseases such as cancer, as well as a discovery platform for new signatures of diseases. It presents many opportunities for new applications, especially in the diagnostics space. Many of the problems we are working on, and anticipate to be working on, have a strong statistical analysis component, requiring the careful processing of hundreds of thousands of antibody binding event measurements generated from our ultra high-density peptide arrays.
The Bioinformatics Scientist will be responsible for the development of statistical tools and scientific applications for analyzing large-scale proteomic data derived from the OneTest assay. Utilizing techniques from statistics, computer science, and machine learning, he/she will work closely with bioinformaticians and scientists on developing algorithms and technical infrastructure to support HealthTell’s proprietary products, facilitate decision-making, and improve business operations and quality control. This position will also entail clinical data management and analysis, and publication of important results.
This is an opportunity to join something with huge potential on the ground floor and make an impact quickly.
Create and refine statistical algorithms to accurately classify patients according to disease status based on their antibody signature.
Develop statistical programs and scientific applications for data analysis and presentation of immunosignaturing results.
Develop code employing statistical and machine learning technologies to process and analyze large-scale proteomic data from the OneTest assay.
Curate, quality control, manage, and analyze clinical datasets.
Actively participate in writing Statistical Analysis Protocols (SAP), and support the team for refining and executing the SAPs.
Actively participate in the development of assay, process design, and manufacturing quality control metrics and optimization.
Collaborate on exploring promising diagnostic applications for OneTest.
Support publication and presentation of the analytic results.
Required - Skills, Knowledge and Abilities:
Proficient in R and other statistical tools.
Significant experience and knowledge of computer systems, programming languages such as Python, and database platforms such as Postgres.
Must work effectively in a team environment as well as independently.
Strong communication and documentation skills.
Solid organizational, problem-solving, and presentation skills.
Demonstrated ability to communicate statistical findings to non-statisticians..
Basic knowledge of molecular sciences and genomics/proteomics.
Enthusiasm for working in a fast moving collaborative environment; ability to deliver projects on short time frames.
Required – Education and Experience:
PhD (or Masters degree with minimum of 5 years industry background) in Biostatistics, Mathematics, Computational Biology or related discipline.
2-5 years of relevant experience is desired.
Desired – Additional Qualifications:
Experience with clinical trial design is highly desired.
Knowledge of SAS and/or JMP is highly desired.
Experience developing classification algorithms and methods such as SVM, random forest, and logistic regression, especially for use as diagnostic tests
Experience analyzing high-density arrays of any type.
Knowledge of immunology and/or oncology.
Familiarity with NumPy, SciPy, and Pandas.