Bioinformatician/Research Data Analyst 1

Stanford University•Stanford, CA

1d•Hybrid

About The Position

Situated in Stanford's highly dynamic research environment, The Kalbasi Lab studies the interface between cancer and the immune system to gain new insights that will inform advances in cancer immunotherapy and radiation therapy.. The lab has a special interest in T cell therapies (CAR-T, TCR-T, TIL) and cytokine therapies, and a disease focus on sarcoma, melanoma and other solid tumors. To achieve this goal, the Kalbasi lab is by necessity versatile and multidisciplinary. The lab integrates cell and protein engineering, T cell immunology, cancer biology, cell signaling, genomics and early phase clinical trials. Each of these approaches has the potential to generate large datasets that require computational analytic approaches. This includes genomic sequencing data (RNA-seq, ATAC-seq, TCR-seq, CITE-seq, whole exome-seq), including bulk and single cell approaches, proteomic data (e.g. phosphoproteomics, cytokine data), and some clinical data. The lab also seeks to employ AI and machine learning approaches for protein design. The Kalbasi Laboratory is seeking a Computational Biologist /Bioinformatician/Research Data Analyst 1 to manage and analyze high throughput next generation sequencing data from samples acquired from laboratory experiments, clinical trials of cancer patients, and//or publicly available data, as well as to participate in new protein design strategies using emerging AI-tools. The Kalbasi laboratory is administratively situated within the Stanford University School of Medicine's Department of Radiation Oncology and the Stanford Cancer Institute. As an NCI-designated Comprehensive Cancer Center the Stanford Cancer Institute is a dynamic and stimulating place to work as it maintains the highest level of scientific rigor, institutional support and coordination for the complete range of cancer-related research, including basic, translational, clinical and population-based science.

Requirements

Bachelor's degree or a combination of education and relevant experience.
Experience in a quantitative discipline such as economics, finance, statistics or engineering.
Substantial experience with MS Office and analytical programs.
Strong writing and analytical skills.
Ability to prioritize workload.
Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.
Occasionally use a telephone.
Rarely writing by hand.
Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide

Nice To Haves

Bachelor's, MS, or PhD degree in Bioinformatics, Computation Biology or Computer Science or a related field with two years of relevant experience.
Strong background in bioinformatics and biostatistics, including experience with analysis of next-generation sequencing data.
Ideal candidates will also have experience with machine learning/AI tools, and experience with or interest in protein design.
Experience and comfort in working within a UNIX/Linux environment.
Background in programming languages such as Python, R, Matlab, Perl.
Good communication and team skills and fluency in both spoken and written English.
Knowledge of techniques for analyzing tumor purity and clonal heterogeneity.
A background in oncology and/or immunology is preferred.
Knowledge of Monte Carlo based statistical methods.
Background in cancer biology.
Familiarity with predictive modeling and machine learning.
Skills in machine learning and data analysis software.

Responsibilities

Collect, manage and clean datasets.
Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data, and for protein design.
Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.
Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.
Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.
Analyze data processes in documentation.
Collaborate with faculty and research staff on data collection and analysis methods.
Provide documentation based on audit and reporting criteria to investigators and research staff.
Communicate with government officials, grant agencies and industry representatives.
Other duties may also be assigned