Lead advanced statistical and computational methods for drug development, develop reusable tools, and contribute to scientific publications and external collaborations.
Compensation
This position's reasonably anticipated salary range is $125,600 - $164,900 per year.
Responsibilities
Develop and apply advanced computational and statistical methods, including machine learning, AI, and scenario evaluation using modern simulation approaches, to inform design, analysis, and decision-making across development.
Build robust, well-engineered, reusable tools and workflows that bring these methods into routine use, with attention to reproducibility and software quality.
Bring a quantitative lens with appropriate rigor to emerging problems such as synthetic and external control data, causal inference, and digital-twin or simulation-based approaches.
Partner with statisticians and cross-functional colleagues to identify where computational and data-driven methods add the most leverage, and translate complex approaches into clear insight for technical and non-technical audiences.
Contribute to internal capability building by sharing tools, code, and methods across the team and wider organization.
Requirements
Doctoral degree in statistics, biostatistics, mathematics, computer science with a strong quantitative/statistical component, or a closely related discipline
Build and maintain intelligent systems, validation frameworks, synthetic data generation models and monitoring pipelines that keep the data ecosystem healthy for Apple’s AI and ML features.
Locations (4)
Austin, Texas, United States
Cupertino, California, United States
New York City, New York, United States
San Diego, California, United States
Responsibilities
Build and maintain intelligent systems, validation frameworks, synthetic data generation models and monitoring pipelines
Own the health of training and validation datasets
Build and maintain intelligent systems, validation frameworks, synthetic data generation models and monitoring pipelines to ensure data quality for AI/ML models across Apple’s Wallet, Payments, and Commerce products.
Locations (4)
Austin, Texas, United States
Cupertino, California, United States
New York City, New York, United States
San Diego, California, United States
Responsibilities
Own the health of the data ecosystem that underpins ML and GenAI features
Build validation frameworks and observability metrics
Lead telemetry analysis for model performance and data drift
Amii is seeking a Research Associate to contribute to SPAARC (Space-ready Principled Adaptive-AI via Renormalization-based Compression), a research project in collaboration with the European Space Agency (ESA), focusing on physics-inspired approaches to neural network compression.
Responsibilities
Contribute to developing the theoretical foundations for applying renormalization-group (RG) concepts to knowledge distillation and neural network compression.
Develop and evaluate machine learning models and algorithms for neural network compression and knowledge distillation.
Design and conduct numerical experiments to investigate model scaling, architectural transformations, and compression limits.
Develop research software and experimental pipelines using modern machine learning frameworks.
Analyze experimental results, including statistical uncertainty, robustness, and sensitivity to architectural changes.
Lead data science efforts on the PREVENT pAKI project, developing federated and pooled machine learning models to predict postoperative acute kidney injury across multiple hospitals.
Locations (3)
New York City, New York, United States
Los Angeles, California, United States
Nashville, Tennessee, United States
Responsibilities
Build and curate analytic cohorts from OMOP-mapped and perioperative data warehouse sources
Develop and validate prediction models including gradient-boosted trees and graph neural networks
Implement federated and pooled models across three institutions
Apply causal inference and reinforcement learning to identify sub-phenotypes
Maintain version-controlled, reproducible code and documentation
Radiance Technologies is an employee-owned company with benefits that are unmatched by most companies in the Dayton, Ohio area. Employee ownership, generous profit sharing, competitive salaries, full health/dental/life/vision insurance benefits, educational reimbursement, interesting assignments, and a pleasant work environment combine to make Radiance a great place to work and succeed.
We are searching for a talented analyst to join our Modeling, Simulation, & Analysis (MS&A) team in support of various missions in Dayton, OH. The candidate will be expected to enhance analytical efforts that are centered around the Advanced Framework for Simulation, Integration and Modeling (AFSIM) Modeling & Simulation (M&S) framework by tackling complex decision-making challenges using scientific methods like mathematical modeling, statistics, and optimization. The candidate will assist in applying optimization techniques and tools to high-fidelity engineering-level analysis of conceptual systems and few-on-few engagement-level simulations. The candidate will also assist in developing analytical procedures and tools for large-scale experimentation. The candidate’s efforts will directly impact the performance and effectiveness of military operations through deep analysis, simulation, and optimization. The candidate will work closely with both Government stakeholders and Radiance teammates across the country supporting AFRL, NASIC, and other Intelligence Community (IC) missions. We are focused on finding innovative self-starters who can adapt to a fast-paced, high-profile analytical effort.
Required Skills/Qualifications
Strong communication and organizational skills
Bachelor’s degree or higher in Mathematics, Statistics, Data Science, Operations Research, or related field
US citizenship with the ability to obtain and maintain a TS/SCI security clearance
Minimum of a SECRET Collateral security clearance
Required Experience
Minimum of 4 years of analytical experience
Minimum of 2 years of experience in Python
Experience using analytical techniques such as descriptive statistics, correlation, analysis of variance, and optimization
Desired Qualifications/Experience
Experience in development and execution of DOE-based analysis plans
Experience using analytical techniques such as uncertainty quantification, sensitivity analysis, and meta-model development
Experience working with statistical software such as JMP and MATLAB
Experience working with DOE software such as Dakota, PIANO, and HAL
Experience working with MS&A tools such as AFSIM, NGTS, and ITASE
Experience using modern agile and CI/CD practices
Familiarity with DoD security practices
Radiance Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
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