ML Scientist - Sr. Consultant level

<h3>Company Description</h3><p>Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.</p><p>Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.</p><h3>Job Description</h3><p>We are in search of a Machine Learning Scientist to implement our machine learning projects and foster innovation across Visa's key products and services. As a senior figure within our organization, you'll lead the creation and implementation of advanced machine learning solutions to tackle intricate business problems. This role provides a thrilling chance to significantly contribute to Visa's strategy of being a global leader in data-driven services. The ideal candidate should possess a solid academic background and exhibit exceptional software engineering abilities. They should be a proactive individual who can handle ambiguity, has a keen eye for detail, and possesses outstanding collaborative skills.</p><p><strong>Responsibilities:</strong></p><ul><li>Develop and execute the AI/ML strategy in alignment with the company's overall goals and objectives, identifying opportunities for leveraging machine learning to improve products and drive business growth</li><li>Collaborate with cross-functional teams including product management, engineering, and business development to define requirements, prioritize projects, and ensure successful execution of machine learning initiatives.</li><li>Oversee the end-to-end development and deployment of machine learning models and algorithms, from data collection and preprocessing to model training, evaluation, and deployment. This includes development of custom algorithms as well as use of packaged tools based on machine learning, data mining and statistical techniques.</li><li>Define strategic &amp; tactical goals for the team as well as key measurable metrics to evaluate the impact of machine learning initiatives, and provide regular updates and reports to senior leadership on progress and outcomes.</li><li>Stay abreast of the latest advancements in machine learning, artificial intelligence, and related fields, and assess their potential impact on the business.</li><li> Foster a culture of innovation, collaboration, and continuous learning within the machine learning team, promoting best practices, methodologies, and tools.</li><li>Work with product engineering to ensure implementability of solutions. Deliver prototypes and production code based on need.</li><li>Experiment with in-house and third party data sets to test hypotheses on relevance and value of data to business problems.</li><li>Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.</li><li>Devise and implement methods for efficiently monitoring model effectiveness and performance in production.</li><li>Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.</li><li>Contribute to development and adoption of shared predictive analytics infrastructure</li></ul><p> </p><p><em>This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.</em></p><h3>Qualifications</h3><p>Basic Qualifications:</p><ul><li>5 or more years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD</li><li>PhD in Computer Science, Operations Research, Statistics or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.</li><li>Track record of mentoring small teams in a collaborative setting</li><li>Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.</li><li>Ability to program in one or more scripting languages such as Perl or Python and one or more programming languages such as Java, C++ or C#.</li><li>Experience with one or more common statistical tools such SAS, R, KNIME, Matlab.</li><li>Excellent understanding of algorithms and data structures.</li><li>Excellent analytic and problem solving capability combined with am