Manager, Visa Consulting and Analytics

Job Description The Intelligence & Data Solutions (IDS) team is looking for a manager to lead, design, and implement high-impact analytical and machine learning solutions. This role is a unique blend of technical leadership, hands‑on data engineering, and hands‑on advanced modeling. The ideal candidate for the IDS team is a talented data scientist with a passion for building robust Machine Learning models from the ground up, starting with raw, complex data. You will not only develop the model but wrangle messy data and deploy scalable solutions that solve real‑world business in Latin America. Responsibilities Hands‑on in the end‑to‑end lifecycle of machine learning projects, from business problem definition and data sourcing to model development, validation, and deployment. Perform and oversee hands‑on data wrangling and feature engineering, parsing and aggregating messy, incomplete, and unstructured data to create clean, model‑ready datasets. Design machine learning models using techniques such as Linear & Logistic Regression, Decision Trees, Random Forests, K‑Nearest Neighbors, K‑Means Clustering, LLM, XGBoost, Neural Networks, to address complex business problems. Manage and query large, complex datasets from big data platforms (e.g., Hadoop, PySpark, Presto, SQL, Hive), ensuring data integrity and efficiency. Translate complex business problems into technical roadmaps and communicate analytical results and insights effectively to both technical and non‑technical stakeholders. Mentor and guide data scientists, fostering technical growth and ensuring the delivery of high‑quality analytical solutions. Leverage Generative AI tools to increase the team’s efficiency and promote knowledge sharing across the organization. Develop and grow exceptional internal and external client relationships, defining priorities and setting the strategy for solution development. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. Qualifications Basic Qualifications 7+ years of relevant work experience in developing machine learning models with a Bachelor's degree, OR 4+ years with an Advanced Degree (Master or PhD). Post‑graduate degree in a quantitative field such as Statistics, Mathematics, Data Science, Operational Research, Computer Science, Informatics, Economics, or Engineering (Master or PhD). Minimum of 5+ years of hands‑on expertise in applying and deploying Machine Learning solutions to solve business problems, with proven experience in the complete model lifecycle (development, validation, and production deployment). Deep theoretical and practical knowledge of machine learning techniques, including but not limited to Linear/Logistic Regression, Decision Trees (Random Forests, XGBoost), Clustering, and Neural Network. Strong programming proficiency in Python and its data science ecosystem (e.g., Pandas, Scikit‑learn, NumPy, PySpark) for building and implementing ML models. Proven ability to perform data manipulation and feature engineering on large, complex datasets using distributed computing platforms (e.g., Hadoop, PySpark) and database systems (SQL, Hive). Experience in presenting complex analytical results and ideas to stakeholders and tailoring communication to various audience levels. Fluency in English (spoken/written). Preferred Qualifications Experience leading or mentoring technical teams. Deep understanding of the Payments and Banking industry. Experience with data visualization tools such as Tableau. Expert knowledge of data, market intelligence, business intelligence, and AI‑driven tools and technologies, with demonstrated ability to incorporate new techniques to solve business problems. Portuguese language skills are a plus. Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. #J-18808-Ljbffr