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Support analytics projects across products, sales, operations, and tech teams. Deliver model strategy roadmaps to continuously improve accuracy and functionality of forecasts and budget management. Drive delivery of analytics including sales forecasting, operations forecasting, and segmentation, as well as crafting data stories and presentations of findings to stakeholders. Collaborate across functions and Lines of Business to drive Analytics and Insights. Work with Sales, Operations, and Product teams to understand business needs and develop analytics solutions. Prepare and deliver presentations summarizing insights and conclusions to senior leaders. Review existing reports on private companies, industries, and macroeconomic conditions to identify relevant themes and risks. Summarize key takeaways and implications for priority merchant categories and consumer spending for stakeholders. Use report insights to support forecasting and planning discussions to provide context, explain drivers, and highlight watch items. Maintain and refresh a curated library of approved reports and reference materials used by the team.\nQUALIFICATIONS:\nMinimum education and experience required: Master's degree in Statistics, Mathematics, Information Science, or Business Analytics or related field of study plus 3 years of experience in the job offered or as Data Scientist Associate, Data Analyst, or a related occupation.\nSkills Required: This position requires three (3) years of experience with the following: Developing and optimizing SQL queries to extract and manipulate datasets for business intelligence purposes including querying and joining data across various levels and environments; Creating Python scripts for data analysis, automation, and the development of machine learning models to streamline data processing; Conducting research on transactions using regular expression rules to query data in Snowflake and AWS Athena and evaluating consumer behavior to predict spending at a merchant; Developing sales and operational forecasts using stats and Machine learning models such as regression, time series, XGBoost, or random forest models to enhance sales capabilities and drive business growth; Validating and enhancing robust models to improve strategic planning and risk assessment; Improving, fine-tuning and implementing machine learning algorithms to improve predictive accuracy and automate decision-making; Using customer transaction data to develop personalized targeting strategies, monitoring campaign performance and improve customer engagement; Using PitchPro+ in Microsoft PowerPoint to create executive level presentations; Presenting analysis methodology and results to non-technical stakeholders; Designing and developing interactive Excel and PowerPoint reports with advanced functionalities including lookup, index match, and calculations for non-technical stakeholders.\nJob Location: 450 W 33rd St, New York, NY 10001.\nFull-Time. 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