• Plan, manage, control, and perform data processing and management activities for assigned projects. • Lead assigned data processing and management projects by applying project management skills, data processing, and management techniques. • Manage timelines and coordinate activities for assigned projects. • Specify database validation checks for assigned studies. • Lead efforts in developing and maintaining standard database validation checks. • Define and monitor clinical trial data flow and quality control (QC) processes. • Perform clinical trial site monitors’ training on data flow and QC processes. • Write clear queries on missing data and data points failing pre-defined range checks. • Perform QC procedures on assigned database during the trial and additional database closure checks at the end of the study. • Train and supervise Data Entry personnel and junior data management personnel on study procedures.

• Work cross-functionally to perform source-to-target mappings between incoming data (ex. EHR) and our data model • Help develop scalable models to automate mapping source-to-target data • Evaluate and validate data from various sources to ensure accuracy, completeness, and reliability • Contribute to the development and extension of ontologies, vocabularies, and data standards that underpin our platform • Identify and evaluate informatics tools, databases, and content sources to enhance the product user experience • Perform quality control checks and become proficient in querying data stored in our regulatory-grade platform • Provide critical input into the design of data architectures, algorithms, and analysis workflows

• Lead the data strategy and continue to evolve Monzo for our customers to make it magically simple to manage money day-to-day • Develop and execute best practices for experimentation enabling our team to make data informed decisions • Collaboratively set standards and work with data across Monzo, fostering knowledge sharing and continuously improving data practices. • Work closely with leaders in product, engineering, design and research to build and iterate on product ideas

Role Description Deep Origin is seeking a Scientist with strong expertise in small-molecule docking and benchmarking, molecular dynamics (MD) simulations, and free energy perturbation (FEP), machine learning, to support a transformative ARPA-H initiative. You'll lead the design of robust simulation workflows and analyze protein-ligand structures across a large target panel to support predictive modeling for therapeutic discovery. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. Qualifications - Ph.D. in computational chemistry, structural biology, biophysics, or related field. - 2+ years of postdoctoral or industry experience in structure-based modeling. - Hands-on expertise with FEP (RBFE/ABFE), including best practices around setup, sampling, and analysis. - Proficiency with one or more simulation platforms (e.g., OpenFE, GROMACS, AMBER, NAMD). - Hands-on experience with RDKit and related cheminformatics tools, and with machine learning methods (RF, gradient boosting, SVM, linear models, Chemprop) for molecular property modeling. - Strong understanding of protein-ligand binding, structure selection, and conformational variability. - Programming experience in Python, and familiarity with tools like MDAnalysis, PyMOL APIs, or MDTraj. Requirements - Analyze tens to hundreds of protein targets relevant to ADMET and off-targets, focusing on conformations, binding site flexibility, and ligand-bound states to guide structure preparation and ensemble design. - Run and refine small-molecule docking, MD, and FEP (RBFE and ABFE) simulations using state-of-the-art tools. - Apply alchemical transformations and advanced sampling strategies to build robust, well-converged, and reproducible FEP workflows for accurate binding free energy predictions. - Apply cheminformatics tools (e.g., RDKit, scikit-learn, etc.) for molecular representation and descriptor generation, and with machine learning methods, including random forests, gradient-boosted trees, SVM, linear/regularized regression, and Chemprop, for molecular property prediction and model validation. - Collaborate with ML and experimental teams to integrate structure-based insights across discovery pipelines. - Communicate progress, technical findings, and challenges across internal and external teams. - Stay current with advances in structure-based binding affinity prediction methods and best practices, and integrate relevant developments into ongoing work. Nice to have - Experience benchmarking across multiple PDB entries or conformational states. - Prior work integrating structural modeling into machine learning pipelines. - Familiarity with MM/GBSA, docking scoring functions, or clustering methods. - Experience using Unix-based HPC environments, workload managers (e.g., SLURM, etc.), and optionally AWS. - Comfort managing large-scale simulation data for modeling or analysis. Benefits - Deep Origin builds modern infrastructure for computational science at the interface of biology, chemistry, and AI. - As part of our ARPA-H program, you’ll shape the future of structure-based modeling for therapeutics.