Bioinformatics / Knowledge Graphs Researcher

About the Role

SandboxAQ’s AI Simulation team develops new drugs and materials using a spectrum of AI and physics-based computational solutions. We are seeking an experienced and innovative Bioinformatics / Knowledge Graphs Researcher to amplify our ability to make inferences about biology by utilizing multimodal data ranging from various “omics” sources to physics-based simulations. The successful candidate will show strong ability in computational biology, including knowledge of cutting-edge machine learning techniques, particularly involving large graph data structures such as knowledge graphs. These skills will be leveraged within a seasoned, agile, and multi-disciplinary group, including drug hunters with an excellent track record in drug discovery, computational chemists, physicists, AI experts, bioinformaticians, and software engineers.

What You'll Do

• Create and implement software to construct and perform operations on biomedical knowledge graphs, drawing from simulated, omics, and literature data.
• Identify relevant data sources and take responsibility for data ingestion, QC, and validation.
• Contribute to ongoing research towards applications of the above, including for biomarker ID, patient stratification, and toxicity prediction.
• Research and implement novel bioinformatics and deep learning algorithms for deciphering human genetic variants, gene regulation, gene expression, and disease pathways by using information from clinical phenotypes and medical records, multi-omics, single cell, proteomics, and genomic data.
• Present to and interact with anyone who needs to understand your work, including clients, other scientists, and non-technical team members.
• Write patents, journal articles, and whitepapers. Speak at conferences and in pitch meetings.
• Work closely with external partners to understand their current challenges for Drug Discovery projects, identify relevant data sets, and research requirements to drive research partnerships forward.
• Vastly improve drug discovery and development on a social scale. Help make better drugs, and help make the tools to do so.

About You

• PhD in a relevant field (bioinformatics, computational biology, computer science, high performance computing or similar).
• 1-5 years of relevant experience including hands-on experience in the private sector working on projects related to one of: bioinformatics, statistical genetics, computational biology, machine-learning, or knowledge graphs.
• Experience processing and curating large-scale omics, clinical data, medical records, and scientific literature data.
• Experienced with common python toolkits for scientific computing (e.g. pandas, numpy, scipy), machine learning (e.g. scikit-learn, pytorch), and bioinformatics (e.g. biotite, biopython).
• Experience with secondary and tertiary analysis of sequencing data related to DNA sequencing, RNA-seq, epigenomics, functional genomics, Single-cell and spatial omics, Single-cell CRISPR screens
• Experience with Bioinformatics tools and pipelines (e.g. BWA, GATK, Samtools, Bedtools, Seurat, Scanpy, Nextflow, Apache Airflow)
• Familiarity running analyses and training models on high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research.
• An interest in solving scientific problems in chemistry and biology via computational and data-driven methods.
• A drive to cooperate with colleagues to identify problems and communicate technical solutions in an accessible manner.
• Hands-on mentality & comfortable with getting deep into the technical weeds of highly complex problems, and a track record of driving projects to completion.

Nice to Have

• Familiarity with cloud-based platforms (e.g. Google Cloud Platform, AWS) for data processing and storage. 
• Demonstrated work on building a pipeline for processing and working with “omics” datasets.
• Knowledge of Genomic databases (e.g. 1000 Genomes, UK Biobank, GTEx, TCGA)
• Knowledge of biomarker ID and toxicity prediction.
• Experience in clinical development.
• Experience in immunology.
• Excellent publication record.
• Willingness to travel less than 25% to conferences, offsites, customers, and internal meetings.

The US base salary range for this full-time position is expected to be $154k - $216k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.