<div><p><strong>Powering Performance Marketplaces in Digital Media</strong></p> <p>QuinStreet is a pioneer in powering decentralized online <strong>marketplaces that match searchers and “research and compare” consumers with brands</strong>. We run these virtual- and private-label marketplaces in one of the nation’s largest media networks.</p> <p>Our industry leading segmentation and AI-driven <strong>matching technologies help consumers find better solutions and brands faster.</strong> They allow brands to target and reach in-market customer prospects with pinpoint segment-by-segment accuracy, and to pay only for performance results.</p> <p>Our campaign-results-driven matching decision engines and optimization algorithms are built from over 20 years and billions of dollars of online media experience.</p> <p>We believe in:</p> <ul> <li>The direct measurability of digital media.</li> <li>Performance marketing. (We pioneered it.)</li> <li>The advantages of technology.</li> </ul> <p>We bring all this together to deliver truly great results for consumers and brands in the world’s biggest channel.</p> <p> </p></div><p><strong><span>Job Category</span></strong></p> <p>We’re looking for an extremely smart Algorithms Engineer to become a core member of the team that creates algorithms for pricing, targeting, demand fulfillment, and fraud detection in our marketing platform.</p> <p> </p> <p><strong>Responsibilities</strong></p> <ul> <li>Use your expertise in machine learning and statistical learning techniques such as classification and regression to produce models applicable to pricing and targeting.</li> <li>Apply mathematical optimization techniques to solve network problems.</li> <li>Experiment and build models that improve the capabilities of our marketing platform and allow for increases in yield.</li> <li>Work with our Engineering and Business teams to implement scalable, reliable and useful solutions for our various business units.</li> <li>Run numerous experiments in a fast-paced, analytical culture so you can quickly learn and adapt your work.</li> <li>Get the satisfaction of knowing that you helped connect millions of visitors to our affiliates and internal properties to the products and services they are searching for.</li> </ul> <p><strong>Requirements</strong></p> <ul> <li>2+ years’ experience in software development</li> <li>MS in Computer Science or related area, or relevant work experience</li> <li>Deep experience in applied machine learning, statistical learning, mathematical optimization and scalable computing</li> <li>Experience with both supervised and unsupervised learning on large datasets</li> <li>Succeed as an individual but to collaborate and strive for the success of your team</li> <li>Strong sense of ownership in everything you build</li> <li>Strong experience building production level code (Python is a plus)</li> </ul> <div> <div><span>The expected salary range for this position is $140,000 USD to $170,000 USD annually. This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. The salary may be adjusted based on applicants geographic location. The position is also eligible to receive performance bonus or commission and equity in the form of restricted stock units. This position is eligible to participate in the Company’s standard employee benefits programs, which currently include health care benefits; (2) retirement benefits; (3) the amount of paid days off (paid sick leave, parental leave, paid time off, or vacation benefits); (4) any other tax-reportable benefits.</span></div> <div> </div> <div><span>#LI-REMOTE</span></div> </div> <div> <div> </div> </div><div><p>QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law.</p> <p>Please see QuinStreet’s Employee Privacy Notice <a href="https://www.quinstreet.com/employee-privacy-notice/" target="_blank">here.</a></p> <p> </p> <p> </p></div>