Analyst-Data Analytics

Job Description

Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. Function Description: The Prospect Direct Mail Analytics team is part of the Analytics, Investments and Marketing Enablement (AIM) team within Global Commercial Services Marketing, American Express. AIM team is responsible for targeting, acquiring, engaging, and retaining commercial customers over online and offline channels and delivering world-class analytics, insights and data products for the Global Commercial Services (GCS) business. In this role, the incumbent will lead the Prospects Direct Mail Analytics team within AIM. Purpose of the Role: The Analyst will own the end-to-end analytics required for executing Commercial Prospects Direct Mail campaigns and would be responsible to profitably drive growth in commercial acquisition and charge volume through the Direct Mail channel. The Analyst will be challenged with designing and creating world class prospect marketing analytics solutions by leveraging machine learning and advanced methodologies. The person will be responsible for performing strategic analyses, synthesizing conclusions, and communicating recommendations to partners aimed at driving revenue through acquisitions. The ideal candidate can drive strategic decision making and execute new strategies via advanced analytics, disciplined test & learn, and effective partnership. The position is part of a highly collaborative environment, interacting with and influencing partners across the Global Commercial Services business at American Express. Responsibilities: Drive profitable acquisitions in Direct Mail channel by meeting ROI / Acquisition / Revenue goals with optimization, experimentation and analytics driven insights. Define, Design, Create, and Implement data science & analytical solutions required throughout the life cycle of a Direct Mail campaign starting from lead generation all the way to performance measurement. Collaborate with stakeholders within GCS Prospect marketing, Finance and investment optimization on various initiatives including setting goals for the channel / influencing data-driven strategy changes / introducing offer personalization etc. Researching and evaluating new commercial data sources working with external data vendors to improve data quality. Creating data segmentation & optimization strategies for targeting profitable prospects with the right product/incentive in the Direct Mail channel Translate business problems into Machine Learning problems. Collaborate with Decision Science teams to quantitatively determine the value of ML models, and ensure key insights are leveraged to create the most suitable ML models to solve the business problems. Collaborate with ML and Tech teams to manage, guide and build analytical solutions to improve targeting efficiency in Direct Mail channel. Minimum Qualifications: Bachelors degree in quantitative field (e.g. Mathematics, Computer Science, Physics, Engineering, Finance and Economics). Demonstrated ability to lead cross-functional teams directly or indirectly to achieve key business outcomes. Strong programming skills are required. Experience with BIG DATA PROGRAMMING LANGUAGES (HIVE, PIG, SPARK), PYTHON (or R or JAVA). Expertise or ability to pick up strong SQL skills. Strong technical and analytical skills with the ability to apply both quantitative methods and business skills to create insights and drive results, such as A/B testing analysis. Strong analytical/conceptual thinking acumen to solve unstructured and complex business problems and articulate key findings to senior leaders/stakeholders in a succinct and concise manner. Demonstrated ability to work independently and across a matrix organization partnering with capabilities, marketing, decision sciences, risk teams and external vendors to deliver solutions at top speed. Preferred Qualifications: Master s in quantitative field (e.g. Mathematics, Computer Science, Physics, Engineering, Finance and Economics) or MBA with quantitative background. Strong knowledge of machine learning techniques, including XGBoost, Decision Trees and NLP models. Knowledge of commercial data experience is a plus. :