Senior Data Scientist at Protege

, , United States -

Full Time

Start Date

Immediate

Expiry Date

10 May, 26

Salary

0.0

Posted On

09 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Statistical Analysis, Data Validation, Data Quality, Data Diversity, Collaboration, Data Preprocessing, Evaluation Frameworks, Bias Detection, Large Datasets, AI Model Training, Data Bottlenecks, Data Scorecards, Synthetic Data Generation, Performance Metrics, Cross-Functional Collaboration

Industry

technology;Information and Media

Description

Company Overview: We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data. Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech. We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. Role Overview: Data is the foundation of AI performance, and we believe model quality starts with data quality. You’ll be at the heart of shaping how we curate, assess, and prepare the training data that powers real-world AI systems. We’re seeking a Senior Member of the Core Data Team to lead the evaluation and optimization of large-scale datasets used to train state-of-the-art AI models. In this role, you’ll help define what "high-quality data" means in practice, using statistical, computational, and ML-driven methods to ensure our data is diverse, representative, and high-impact. You’ll work closely with research and engineering teams to improve model performance through better data. This is an ideal role for someone with a PhD in machine learning, CS, or a related applied field who is passionate about the role of data in AI training and excited to advance Protege’s mission to become the ubiquitous platform for AI training data. Key Responsibilities: Design and apply statistical and machine learning methods to curate, filter, and enrich large-scale unstructured datasets Develop frameworks to assess data diversity, duplication, and informativeness. Design statistical approaches to de-risk training datasets Collaborate with model training teams to identify data bottlenecks and optimize dataset performance. Emphasis on ability to collaborate with large foundational models and smaller startups Provide leadership on data quality strategy and shape internal best practices Evaluate external datasets for integration, focusing on scalability, quality, and relevance to model performance. Help build data scorecards Contribute to research and development of tools that automate data preprocessing and validation About You: PhD or equivalent Master's Degree + 4+ years industry experience in machine learning, economics, mathematics, engineering, computer science, statistics, or a related quantitative field Strong understanding of AI model training pipelines, including pre-processing and evaluation Experience working with large, unstructured datasets, especially text Background in statistical analysis, bias detection, and data validation Able to identify high-impact problems and drive independent solutions Bonus if you have these attributes: Experience with synthetic data generation or augmentation strategies Publications or open-source contributions in data-centric AI or related areas Experience developing evaluation frameworks or performance metrics for training data Cross-functional collaboration with product, infrastructure, or partnership teams

Responsibilities

You will lead the evaluation and optimization of large-scale datasets used to train AI models. This includes designing statistical methods to curate and assess data quality and collaborating with teams to improve model performance.