> Markdown version of [/jobs/ext/2073512-data-scientist-ii](https://www.wearedevelopers.com/jobs/ext/2073512-data-scientist-ii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist II - **Company:** SUNDAE, INC. - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $114,297.0 - $235,319.0 - **Contract:** Permanent contract - **Skills:** Airflow, Code Review, Machine Learning, Azure Machine Learning, Workflow Management Systems, Software Organization, Pytorch, Apache Spark, Deep Learning, Information Technology, Machine Learning Operations, Software Version Control, Jenkins - **Published:** August 15, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pb8xdfkg86 ## About the Role * 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist, or software engineer with significant ML production experience. * Proficiency in Python programming, with experience using PyTorch or equivalent deep learning frameworks. * Familiarity with distributed compute frameworks such as Spark or Ray; experience with Ray is a strong plus. * Experience with workflow orchestration tools like Airflow, Prefect, or Jenkins. * Bachelor's or Master's degree in Computer Science, Data Science, or a related field, or equivalent practical experience. * Strong understanding of ML theory and principles, capable of reasoning about models from first principles. * Experience in developing and maintaining reproducible ML pipelines and adhering to software development best practices, including version control and code review. * Ability to build scalable tools and platforms that enhance the impact of the entire ML organization. ## Description We are seeking a highly skilled Data & Applied Scientist to join our team and contribute to the advancement of machine learning (ML) systems at Pinterest. This role is pivotal in developing scalable ML measurement systems, causal inference methodologies, and feature understanding platforms that enhance our platform's efficiency and effectiveness. The successful candidate will be responsible for translating research-grade workflows into robust production pipelines, applying causal inference techniques to high-stakes measurement problems, and building tools that empower teams across the organization. You will collaborate closely with ML engineers, product managers, and data scientists to identify opportunities for innovation, design scalable solutions, and establish rigorous standards for ML model development and deployment. Your work will directly impact the quality of our models, the insights derived from data, and ultimately, the user experience on Pinterest., * Translate research workflows such as proxy metrics and staleness models into scalable, production-ready ML pipelines using tools like Airflow, WandB, and Ray. * Apply and operationalize causal inference methods, including propensity scoring, inverse probability weighting (IPW), and targeted maximum likelihood estimation (TMLE), to address complex measurement challenges beyond traditional experimental methods. * Develop self-serve tooling to enable non-experts to derive rigorous causal insights at scale, fostering data-driven decision-making across teams. * Collaborate with ML engineers and product teams to identify opportunities for improving tooling, metrics, and measurement methodologies, leading to significant enhancements in model quality and business outcomes. * Utilize Pinterest's rich metadata and engagement signals to design data-driven frameworks for feature importance analysis, content deindexing, and platform efficiency improvements. * Design, build, and maintain centralized ML platform tools that streamline feature and model creation, evaluation, and trust, ensuring systems operate reliably at scale. * Establish rigorous methodological standards for ML systems and contribute to foundational innovations that elevate the entire ML organization's capabilities. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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