• Extract data from primary and secondary sources and conduct data analysis to generate actionable insights • Develop and maintain databases, data systems – reorganize data in a readable format • Design charts and tables that can easily show the key messages presented to different audiences • Filter Data by reviewing reports and performance indicators to identify and correct problems • Assign numerical value to essential business functions so that business performance can be assessed and compared over periods of time • Prepare reports for management: stating trends, patterns, and predictions using relevant data • Work with engineers, designers and project managers to create engaging and insightful business reviews and reports

Big Picture Bio is a seed-stage techbio company, backed by DSV, building a computational drug discovery platform that constructs causal biological networks from two primary sources: structured extraction of published experimental literature and large-scale primary human single-cell omics data. Our multi-agent AI system reasons over these networks to generate, simulate, and rank mechanistic hypotheses for combination therapies — with system accuracy verified against top-tier researchers at the Allen Institute. We are initially focused on oncology. The Role (remote, timezone-restricted) You will design and build production bioinformatics pipelines for new modalities—spatial transcriptomics, single-cell proteomics, and spatial proteomics—extending our existing scRNA-seq infrastructure. These pipelines feed directly into an agentic hypothesis generation system: the quality of what goes in determines the quality of every therapeutic hypothesis that comes out. You’ll work closely with our Head of AI & Technology (Dr. Francesco Moramarco) and Head of Platform (Dr. Moustafa Khedr) to: - Build end-to-end pipelines (ingestion, QC, normalization, integration, annotation, differential analysis) - Design modality-specific statistics: spot deconvolution, spatial autocorrelation, ADT normalization, protein-RNA joint embedding, segmentation, spillover correction - Extend hierarchical cell type annotation across modalities - Codify best-practice workflows into reusable templates for agent execution - Sanity-check outputs to catch batch effects and artifacts before they propagate

• Leverage Machine Learning and advanced data analytics to identify risks and set the right price for each client; • Analyze and interpret complex data to support strategic decision-making; • Contribute directly to the company’s growth and long-term success through actionable insights.

• Drive technical roadmap to extend risk monitoring across identified threat surfaces • Develop/experiment/ship state-of-the-art prediction models • Use excellent data science practices to iteratively produce high performing models • Create immediate impact through sound and practical deliveries of risk monitors • Work with engineering colleagues to convey findings through data visualizations • Measure, tune and refine existing algorithms to incrementally improve performance • Analyze new and existing data after extracting, transforming and combining it in novel ways • Convey needs to engineering and operations teams to ensure healthy feedback loops • Attract and onboard new talent while preserving and enhancing existing culture • Build strong partnerships and collaborate with other teams across the enterprise • Demonstrate sense ownership and personal accountability for your team's work