Collegeville, , United States
Oligo Data Science Leader
The oligotherapy data science leader role is embedded in and directly supports the Oligo Sciences team whose broad remit is the design, characterization, and early development of oligonucleotide therapies. Oligo data scientists will serve a dual purpose of co-developing a data capture & analysis platform with oligo sciences experts and translating their needs broadly across GSK to build methods/models/capabilities that fulfil them.
Co-design and implement a target tractability system for oligo-guided therapeutic mechanisms such as RNA degradation/interference, and up/down modulation of alternative splicing, poly-adenylation, A>I editing, etc.
Co-develop a target/complex engagement model that combines target sequence, secondary+ tertiary structure, and oligo chemistry to drive binding assay designs for both high-throughput screening and low-latency optimization.
Collaborate effectively with renowned GSK experts in genetics, genomics, computational bio & chem, structural biology and medicinal chemistry, artificial intelligence / machine learning, and digital technologies to build the datasets, tools, models, and infrastructure necessary for oligo design & development.
PhD degree in computational life sciences (e.g. bio/med/chem/stats/informatics) with at least 1 (co-)first and 1 supporting authorship
Over 4 years industry experience (or postdoc + 2 years industry) demonstrated by published articles/patents/talks/awards
Over 2 years experience with genomic data infrastructure for either public or private collections (e.g. ENCODE, TCGA, DepMap, GTEx, cellXgene, UK biobank)
Over 2 years experience with cheminformatic and structural modelling of drug-target binding for small or large molecules validated by real qSAR assays
Demonstrated cross-functional communication, matrixed collaboration, both team and thought leadership
Performed target identification or tractability/prioritization for RNA-modulating approaches or RNA-targeted modalities
Participated in high-throughput or high-content screening for hits followed by low-latency iterations for lead optimization
Developed analytic tools & data pipelines for processing & querying large batches of disease/perturbation/control samples
Biology research focus on either coding or non-coding RNA and its post-transcriptional regulation in various eukaryotic cells/tissues
Chemistry research focus on oligonucleotide modifications: either synthetic sugars/backbones, or natural nucleobase variants
Mastery of statistical learning or predictive modelling with signals and noise in empirical datasets: either observational or perturbational
Mastery of predictive analytics and modern data stack: principal developer on a codebase in Python/R/Julia/C++/Java/Rust, or considerable and consistent contributions to an open-source project, or Top10 placement in life-science-themed competitions on Kaggle/TopCoder/DREAM challenge.
Familiarity with Bayesian statistics and machine learning (ML & DL) with prudent use of suitable methods as projects progress
Familiarity with genetic mutations’ impacts on both coding and non-coding RNA sequences and their associated binding proteins, especially in regulating alternative splicing, poly-adenylation, and RNA chemical modifications like methylation or editing.
Knowledge of 2o and 3o structure and computational/experimental techniques (e.g. Chemical probing, NMR, CryoEM) to determine folding dynamics of eukaryotic RNAs in complex with chaperone RNP and RBPs.
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