Computational Scientist, NGS Data Analysis

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The Bay Area Institute of Science at Altos Labs has an opening for a Computational Scientist in our MultiOmics Data Analysis group. Responsibilities include:

• Collaborate closely with domain knowledge experts to plan and design studies to elucidate molecular phenotypes of cellular health, rejuvenation and reprogramming.
• Develop NGS analysis pipelines covering raw data processing, through to exploratory and statistical data analysis, to hypothesis generation.
• Develop new and apply existing methods for multi-omic data integration.
• Perform integrative analyses of omics and diverse phenotypic data.
• Present and interpret analysis results to inform future experiments and to guide decision making.
• Embed analyses and visualizations in automated reports.
• Build interactive dashboards for data visualization and exploration.
• Partner with other computational scientists to establish automated, robust and efficient analytical pipelines for reproducible research.
• Stay current with and adopt emergent analytical methodologies, tools and applications to ensure fit-for-purpose and impactful approaches.

Who You Are

Minimum Qualifications:

• PhD in a quantitative field (e.g. mathematics, bioinformatics, physics) with significant biological background OR a Life Sciences degree with significant computational experience.
• Advanced understanding of NGS platforms and the underlying technological limitations.
• Extensive experience with building NGS data analysis pipelines (RNA-seq, Ribo-seq, ATAC-seq, BS-seq, Perturb-seq).
• Extensive knowledge of standard tools for analysis of NGS data ( e.g. FastQC, BWA, BEDtools, STAR, TopHat, Cufflinks, Limma, edgeR, DESeq).
• Proficiency in Python and/or R. Hands-on skills using data science packages ( for instance, Pandas, Scikit-learn, NumPy, Tidyverse, Caret).
• Statistical analysis background (e.g. parametric and non-parametric tests for differential expression analysis, enrichment analysis, clustering, dimensionality reduction techniques, power analysis).
• Excellent communication skills. Ability to present complex computational methods to non-experts. 
• Established ability to translate biologists/project team’s scientific questions into analytical strategies and methods.
• Strong collaboration skills and ability to work as part of a team in an international and interdisciplinary environment.
• Outstanding organizational skills and the ability to work independently.

Preferred Qualifications:

• Experience with technologies required to undertake analyses on large data sources or with computationally intensive steps (databases, parallelization, Hadoop, Spark, etc.).
• Experience with code management and collaboration platforms (e.g. GitHub, Bitbucket).
• Experience with development of visualization reports and apps (e.g. Shiny/Dash/ d3).
• Experience with applying machine learning to mine biological datasets for actionable hypotheses.
• Experience with software development.
• Experience with cloud computing.