• 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.

Data Scientist - Pricing Lititz, PA Job Description Job Summary Webstaurant Store, the world’s largest online restaurant supply company, is looking for a full-time Data Scientist to join the Data Science team. The Data Scientists are responsible for supplying statistical rigor to the organization’s analytics projects, aimed at discovering actionable insights and opportunities for growth and optimization. This role can be remote in some US states (See below for remote work requirements) or based in one of our offices (Lititz, PA or Tampa, FL). Remote Work Qualifications - Access to a reliable and secure high-speed internet connection. Cable or fiber internet connections (at least 75mbps download/10mbps upload) are preferred, as satellite connections often cannot support the technologies used to perform day-to-day tasks. - Access to a home router and modem. - A dedicated home office space that is noise- and distraction-free. The space should have strong wireless connection or a wired Ethernet connection (wired connection is preferred, if possible). - A valid, physical address (apartment, suite, etc.). PO Boxes are not supported, as a physical address is required for you to receive your computer equipment. - The desire and ability to work and communicate with other team members via chat, webcam, etc. - Legal residents of one of the following states: (AK, AL, AR, AZ, CT, DE, FL, GA, IA, ID, IN, KS, KY, LA, MD, ME, MI, MN, MO, MS, NC, ND, NH, NM, NV, OH, OK, PA, SC, SD, TN, TX, UT, VA, VT, WI, WV, and WY). We only accept W-2 candidates, H-1B sponsorship is not available. Responsibilities Responsibilities include, but are not limited to: - Advising on analysis and modeling techniques, selecting the best fit for structured & unstructured, complex data, and developing custom analytics models to generate insights that drive value for our customers and unlock competitive advantage for our organization. - Collaborating with our analytics engineers to gather the right information from various sources so that you can implement advanced statistical methodologies and design interventions to optimize our business performance. - Conducting exploratory analysis including hypothesis testing, statistical inference, and statistical analysis (predictive and/or prescriptive). - Partnering with business and technical stakeholders to translate challenging retail business problems into state-of-the-art data science solutions. - Developing processes and tools that monitor, analyze and continuously improve model performance and data accuracy. - Work with other data scientists to design and implement experiments and glean actionable insights from experimental analyses.  - Defend and explain the validity of predictive/ML models to those without a background in data science or statistics. - Actively research new tools and technologies, staying on top of analytics news and best practices. Physical Requirements - Work is performed while sitting/standing and interfacing with a personal computer. - Requires the ability to communicate effectively using speech, vision, and hearing. - Requires the regular use of hands for simple grasping and fine manipulations. - Requires occasional bending, squatting, crawling, climbing, and reaching. - Requires the ability to occasionally lift, carry, push, or pull medium weights, up to 50lbs. Qualifications Experience - Minimum 2 years experience applying data science to pricing and demand problems in ecommerce, retail, or foodservice environments. - Proven ability to build and analyze price response curves or similar models to identify optimal pricing across a wide range of products. - Experience using data to inform pricing strategy and day-to-day pricing decisions, even if not the final decision owner. - Strong understanding of price elasticity, demand forecasting, and trade-offs between margin, volume, and customer acquisition. - Demonstrated ability to identify strategic low-priced items that drive customer traffic, alongside optimizing prices for both low and high AOV products. - Comfortable working with large SKU catalogs and messy real-world data to deliver actionable pricing insights. - Ability to partner closely with merchants, and other teams to translate model outputs into business decisions. - Advanced Python and SQL skills Education This role does not require a degree. We value relevant skills and experience and alignment with our core values above all else. Desired Traits & Skills The ideal candidate will possess the following qualities:   - You Communicate Effectively: Ability to explain ideas simply and confidently to team members both written and verbally.   - You have an Entrepreneurial Spirit: Takes it upon themselves to find ways to add value to the team and business. They are self-motivated and creative in their quest to find actionable insights and new solutions. They can often be found saying “What if we tried this?” or “Maybe this is a better way to do it?” They have a knack for overcoming obstacles.  - You Desire to Grow: Constantly seek to improve themselves and expand their professional knowledge and skill set.  - You’re an inquisitive, out-of-the box thinker who’s continually on the lookout for opportunities to improve and innovate data science solutions. - You have consistently high standards; your passion for quality is inherent in everything that you do. - Familiarity with a variety of supervised/unsupervised/reinforcement learning methods. - Demonstrable portfolio (git repos, kaggle competitions, etc) of machine learning projects. - You have proven ability as a full stack data scientist who develops, experiments, optimizes, and scales models for the production environment. - You connect the how and why, using your knowledge of data science theories and methodologies to derive the best approach that is fit-for-purpose.