Role Description Develop best practices for instrumentation and experimentation and communicate those to product engineering teams to help us fulfill our mission - to bring everyone the inspiration to create a life they love. - Bring scientific rigor and statistical methods to the challenges of product creation, development, and improvement with an appreciation for the behaviors of our Pinners. - Build and prototype analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development. - Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest. - Full-time telecommuting is an option. Qualifications - Master’s degree (or its foreign degree equivalent) in Data Science, Analytics, or closely related quantitative discipline and two (2) years of experience in the job offered or in any occupation in related field, OR - Bachelor’s degree (or its foreign degree equivalent) in Data Science, Analytics or closely related quantitative discipline and five (5) years of progressively responsible experience in the job offered or in any occupation in related field. Requirements - Python - SQL - Machine Learning - A/B Testing - TensorFlow - PyTorch - Jupyter Notebook - Git - Data Analytics - Statistical Inference - Advertising Analytics domain experience - Any suitable combination of education, training, and experience is acceptable. - Full-time telecommuting is an option. Benefits - Salary: $203,000.00 - $235,319.00 per annum - This position is not available for relocation assistance. Company Description Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. - We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. - See also Know Your Rights and Pinterest Policy for more information regarding U.S. roles. - If you require a medical or religious accommodation during the job application process, please complete this form for support.

• Lead complex, cross-functional analytics projects focusing on improving key outcomes • Serve as the strategic lead for the Global Employee Listening Program • Translate complex datasets into "executive-ready" narratives and visualizations • Partner with HRIS and EDE teams to migrate disparate data sources into a unified "single source of truth" • Design and deploy advanced Tableau dashboards and analytics tools for COE partners • Utilize SQL, Python, or R to uncover correlations in data • Coach HRBPs and COEs on data literacy and insights interpretation

Role Description As an Applied Researcher on the Data Science team, you will play a critical role in bringing new products to market. - Prototype innovative ideas, conduct experiments, and iterate quickly to develop data-driven solutions for complex business challenges. - Independently work on end-to-end research projects while actively collaborating with a team of data scientists. - Present and promote results to internal stakeholders and external clients and partners. - Support and improve existing data science products. - Analyze large, complex, and noisy datasets to extract actionable insights and support data-driven decision-making. - Design, implement and evaluate machine learning and statistical methods to perform exploratory data analysis, data storytelling and product development. - Design and execute A/B tests and experiments to optimize product performance and maximize revenue growth. - Pursue patents where applicable. - Contribute to thought leadership in the community with presentations and publications. Qualifications - Independent research experience is mandatory. - A Ph.D. in a quantitative field (e.g. machine learning, statistics, physical science, or quantitative social science) is preferred, or a M.S. in Machine Learning or statistics with at least 3 years of independent research. - Experience using real data to solve real problems. - Expertise in statistical modeling, machine learning, and fluency in the related technical tools. - Interest in applying machine learning to large datasets to solve business problems. - Knowledge of current large language models and generative AI tools and an interest in applying them to data products. - Excellent oral and written communication skills; comfort in a client-facing role. - A track record in contributing to publications and/or presentations in a quantitative field. - Intermediate programming skills. - You should be ready to learn and work with Python, Google Cloud Platform, Scikit-learn, TensorFlow, PyTorch, Spark, BigQuery and SQL.

• Partner with engineering to define the architecture and technical strategy for internal risk systems and tooling. • Partner with Risk, Engineering, Security, Compliance, Product, and Operations to define risk metrics, success criteria, and decision frameworks. • Analyze fraud, scam, and abuse patterns across crypto and fiat products. • Build and maintain reporting that tracks fraud loss, false positives, customer friction, operational efficiency, and control performance. • Help design and architect the data, instrumentation, and decisioning systems that power risk operations and fraud prevention workflows. • Evaluate and iterate on risk rules and controls using rigorous analysis. • Apply statistical methods and selective ML techniques where they materially improve decision quality, operational efficiency, or signal detection. • Conduct deep-dive investigations into incidents, trends, and emerging threats. • Communicate insights clearly and persuasively to senior stakeholders • Help establish data standards, analytical methods, and decision-making practices within the Risk team