3D Data Scientist

Target PR Range: 65-75/hr • Depending on experience Role Summary We are seeking a highly skilled and intellectually curious 3D Data Scientist to join our growing Digital Endpoints team at the intersection of computational science, facial aesthetics, and cutting-edge 3D capture technology. This is a pioneering role: you will be among the first members of the team to operationalize 3D data science capabilities, building on a strong foundation of over 100 validated digital endpoints already developed for 2D images and video. In this role, you will lead the validation of a state-of-the-art 3D capture system, architect robust validation pipelines using photogrammetry-rendered 3D imagery, and collaborate cross-functionally to define and develop the next generation of 3D digital endpoints in the facial region for aesthetics applications. You will sit at the convergence of machine learning, 3D rendering, and scientific rigor — and your work will directly shape how aesthetic outcomes are measured, quantified, and communicated in clinical and commercial settings. This role is equal parts scientist and builder. You must move fluidly between data science workflows and 3D rendering environments, think with both precision and product-mindedness, and bring a strong bias toward innovation without sacrificing scientific integrity. Key Responsibilities 3D Capture Validation • Lead the end-to-end validation of a 3D facial capture system, establishing technical benchmarks for accuracy, repeatability, and clinical relevance. • Design and execute structured validation pipelines using 3D rendered photogrammetry images to evaluate system capabilities across diverse subject populations and capture conditions. • Develop quantitative test protocols and statistical frameworks to assess 3D capture fidelity, geometric precision, and landmark reproducibility. • Document findings with scientific rigor and communicate validation outcomes to technical and non-technical stakeholders. 3D Digital Endpoint Development • Partner with the Digital Endpoints team to define, prototype, and scale a new suite of 3D digital endpoints for facial aesthetics applications, extending the team's existing library of 100+ 2D endpoints. • Translate 3D capture capabilities and mesh data into clinically meaningful, computable biomarkers and outcome measures. • Drive hypothesis generation and experimental design for novel 3D endpoints, balancing scientific validity with practical scalability. • Establish best practices for 3D data preprocessing, surface reconstruction quality control, and feature extraction pipelines. Machine Learning & Modeling • Build and evaluate machine learning models (supervised, self-supervised, and geometric deep learning) applied to 3D facial meshes, point clouds, and photogrammetry assets. • Design experiments to benchmark model performance, generalizability, and robustness across capture systems and patient demographics. • Iterate rapidly on model architecture and training strategies in close collaboration with engineering and science teams. Cross-Functional Collaboration & Innovation • Serve as the technical bridge between the data science team and 3D rendering/capture specialists, translating requirements bidirectionally with clarity and precision. • Collaborate with clinical scientists, product managers, and regulatory stakeholders to ensure endpoints are fit-for-purpose in aesthetic clinical trials and commercial applications. • Champion a culture of experimentation, reproducibility, and continuous improvement across 3D data science workflows. • Stay ahead of the curve on emerging tools, techniques, and literature in 3D computer vision, neural rendering, and digital biomarkers. Required Qualifications • Bachelor's degree or higher in Computer Science, Data Science, Computational Biology, Biomedical Engineering, Computer Vision, or a closely related quantitative field (Master's or PhD strongly preferred). • 3+ years of hands-on experience in data science or machine learning roles, with a demonstrated track record of delivering production-quality work. • Strong proficiency in Python and standard data science libraries (NumPy, SciPy, Pandas, scikit-learn, PyTorch or TensorFlow). • Demonstrable experience working with 3D data formats — including meshes, point clouds, depth maps, or photogrammetry outputs — in a research or applied context. • Deep familiarity with at least one professional 3D rendering or modeling platform such as Blender, Autodesk Maya, or equivalent. • Proven ability to design and execute rigorous validation or benchmarking studies with a statistical foundation. • Strong written and verbal communication skills, with the ability to present complex technical findings to diverse audiences. • Comfortable operating in ambiguous, fast-moving environments with a high degree of autonomy and ownership. Preferred Qualifications • Experience in the aesthetics, dermatology, medical imaging, or clinical digital health domain. • Familiarity with photogrammetry pipelines and tools (e.g., RealityCapture, Agisoft Metashape, or similar). • Exposure to geometric deep learning frameworks (e.g., PyTorch Geometric, Open3D, trimesh). • Experience developing digital endpoints, biomarkers, or outcome measures in a regulated or clinical context. • Knowledge of 3D facial landmarking, surface parameterization, or shape analysis methods. • Experience contributing to or leading cross-functional research and development projects in an industry setting. • Familiarity with version control, MLOps principles, and reproducible research practices (Git, DVC, MLflow, or equivalent). Technical Skills • Category Tools & Technologies Programming Python (primary), R (secondary), SQL Machine Learning PyTorch, TensorFlow, scikit-learn, geometric deep learning 3D Rendering & Modeling Blender, Autodesk Maya, RealityCapture, Metashape 3D Data Processing Open3D, trimesh, PyMeshLab, PCL, point cloud / mesh workflows Photogrammetry 3D mesh reconstruction, UV mapping, texture baking, depth maps Validation & Statistics A/B testing, ICC, Bland-Altman analysis, bootstrap methods Data Infrastructure Git, DVC, MLflow, cloud platforms (AWS / GCP / Azure) Visualization Matplotlib, Plotly, ParaView, 3D scene rendering pipelines Success Metrics / What Success Looks Like in the First 12 Months We believe in setting clear expectations. Here is what exceptional performance looks like in this role: • Timeframe Milestone 0–3 Months Deep understanding of the existing 2D digital endpoint library, data infrastructure, and 3D capture system architecture. Initial validation protocol drafted and under review. 3–6 Months First full validation pipeline for the 3D capture system is operational. Preliminary validation report delivered to scientific and product stakeholders with clear capability characterization. 6–9 Months At least 3–5 novel 3D digital endpoint candidates identified, defined, and in active development or prototyping. Machine learning models benchmarked against defined performance criteria. 9–12 Months First set of 3D digital endpoints validated, documented, and ready for deployment in an aesthetics study or product context. Cross-functional best practices for 3D data science workflows established and socialized across the team.