<div><p><strong>About Pinterest:</strong></p> <p>Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p> <p>Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the <a href="https://www.pinterestcareers.com/our-life/pinflex/">flexibility</a> to do your best work. Creating a career you love? It’s Possible.</p></div><p>Pinterest is the world’s leading visual search and discovery platform, serving over 500 million monthly active users globally on their journey from inspiration to action. At Pinterest, Shopping is a strategic initiative that aims to help Pinners take action by surfacing the most relevant content, at the right time, in the best user-friendly way. We do this through a combination of innovative product interfaces, and sophisticated recommendation systems.  </p> <p>We are looking for a Staff Data Scientist with experience in machine learning and causal inference to help advance Shopping at Pinterest. In your role you will develop methods and models to explain why certain content is being promoted (or not) for a Pinner. You will work in a highly collaborative and cross-functional environment, and be responsible for partnering with Product Managers and Machine Learning Engineers. You are expected to develop a deep understanding of our recommendation system, and generate insights and robust methodologies to answer the “why”. The results of your work will influence our development teams, and drive product innovation. </p> <p> </p> <p><strong>What you’ll do:</strong></p> <ul> <li>Ensure that our recommendation systems produce trustworthy, high-quality outputs to maximize our Pinner’s shopping experience.</li> <li>Develop robust frameworks, combining online and offline methods, to comprehensively understand the outputs of our recommendations.</li> <li>Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners.</li> <li>Work cross-functionally to build relationships, proactively communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest.</li> <li>Relentlessly focus on impact, whether through influencing product strategy, advancing our north star metrics, or improving a critical process.</li> <li>Mentor and up-level junior data scientists on the team. </li> </ul> <p> </p> <p><strong>What we’re looking for:</strong></p> <ul> <li>7+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data.</li> <li>Strong interest and experience in recommendation systems and causal inference.</li> <li>Strong quantitative programming (Python/R) and data manipulation skills (SQL/Spark).</li> <li>Ability to work independently and drive your own projects.</li> <li>Excellent written and communication skills, and able to explain learnings to both technical and non-technical partners.</li> <li>A team player eager to partner with cross-functional partners to quickly turn insights into actions.</li> <li><span>Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience.<br /><br /></span></li> </ul> <p><strong>In-Office Requirement Statement:</strong></p> <ul> <li><span>We let the type of work you do guide the collaboration style. That means were not always working in an office, but we continue to gather for key moments of collaboration and connection.</span></li> <li><span>This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.<br /><br /></span></li> </ul> <p><strong>Relocation Statement:</strong></p> <ul> <li>This position is not eligible for relocation assistance. Visit our<a href="https://www.pinterestcareers.com/pinflex/"> PinFlex</a> page to learn more about our working model.</li> </ul> <p>#LI-REMOTE</p> <p>#LI-NM4</p><div><div><div><p>At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.</p> <p><em><span>Information regarding the culture at Pinterest and benefits available for this position can be found <a href="https://www.pinterestcareers.com/pinterest-life/" target="_blank">here</a>.</span></em></p></div><div>US based applicants only</div><div><span>$163,064</span><span>—</span><span>$335,720 USD</span></div></div></div><div><p><strong>Our Commitment to Inclusion:</strong></p> <div>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. If you require a medical or religious accommodation during the job application process, please complete <a href="https://forms.gle/sS24D4WboEKUZMQK6">this form</a> for support. <div> </div> </div></div>