The Analytics, Investments and Marketing Enablement (AIM) team within Global Commercial Services (GCS) is the analytics engine powering data-driven customer acquisition, engagement, and growth. AIM develops insights, data products, advanced analytical solutions, and AI capabilities that enable smarter decisions and more personalized customer experiences across the Commercial business.
This Senior Analyst role sits within the Customer Corporate card -Xsell team, focused on building the next generation of intelligent cross-sell capabilities for Commercial customers. The team brings together rich customer data, behavioral signals, advanced machine learning, and emerging AI techniques to better understand customer intent, behavioral sequences, and evolving product needs—and translate them into relevant, timely opportunities to deepen customer relationships.
How will you make an impact in this role?
As a Senior Analyst, you will play a key role in developing analytical and machine learning capabilities that help determine what our customers may need next, and when. You will work with large-scale transactional and behavioral datasets, uncover patterns across customer journeys, and develop models that transform these signals into actionable cross-sell strategies.
You will have the opportunity to work on challenging problems spanning intent identification, sequence mining, recommendation systems, predictive modeling, and Generative AI, while partnering with business and technical teams to translate analytical innovation into measurable customer and business impact.
This role is ideal for someone who enjoys solving ambiguous problems with data, has strong hands-on Python and SQLskills, is excited by modern recommendation and sequence-modeling techniques, and wants to help shape how advanced analytics and AI are applied to Commercial customer growth.
- Build Advanced Cross-Sell & Recommendation Capabilities: Design, develop, and iterate machine learning solutions that identify relevant next-product and cross-sell opportunities using customer, product, transactional, and behavioral signals.
- Understand Customer Intent & Behavioral Sequences: Apply techniques such as intent modeling, sequence mining, recommendation algorithms, and representation learning to uncover patterns in customer journeys and identify signals that indicate emerging customer needs.
- Develop Modern ML Models: Explore and apply advanced modeling approaches, including BERT-based architectures, transformer models, TiSASRec and other sequential recommendation techniques, alongside traditional machine learning methods where appropriate.
- Drive Hands-on Analytics: Use Python and SQL extensively to explore large and complex datasets, engineer meaningful features, test hypotheses, build analytical frameworks, and translate insights into scalable solutions. Develop clean, efficient, reproducible analytical workflows.
- Translate Business Problems into Analytical Solutions: Partner with stakeholders to convert complex and sometimes ambiguous business questions into clearly defined analytical problems, measurable hypotheses, modeling approaches, and success metrics.
- Explore GenAI Applications: Evaluate opportunities to leverage Generative AI and LLM-based capabilities, including retrieval-augmented generation (RAG), prompt design, LLM evaluation, and approaches for identifying and mitigating hallucinations, where they can meaningfully enhance analytical solutions.
- Collaborate Across Teams: Partner closely with Marketing, Product, Sales, Technology, and other Analytics teams to connect analytical insights with business strategy and operational execution.
- 2–3 years of relevant experience in data science, machine learning, advanced analytics, or a related quantitative field.
- Bachelor’s degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Engineering, Economics, Data Science, or a related field.
- Strong hands-on Python skills are essential, with experience using Python for data manipulation, exploratory analysis, feature engineering, statistical analysis, and machine learning.
- Strong SQL skills are essential, including the ability to independently work with large and complex datasets, construct sophisticated queries, and perform data extraction, transformation, and analysis.
- Experience with customer intent modeling, sequence mining, recommendation systems, sequential data, NLP, or related techniques.
- Strong understanding of core machine learning concepts, including feature engineering, model evaluation, validation, overfitting, performance measurement, and model interpretability.
- Strong analytical and problem-solving abilities, with the ability to break down ambiguous business problems and translate them into structured analytical approaches.
- Ability to operate effectively in a collaborative, cross-functional environment while demonstrating intellectual curiosity, ownership, and strong attention to detail.
Preferred Qualifications
- Master’s degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
- Exposure to modern deep-learning architectures and algorithms such as BERT, Transformers, TiSASRec, SASRec, or similar sequence/recommendation approaches.
- Understanding of Generative AI and Large Language Model concepts, including prompting, embeddings, Retrieval-Augmented Generation (RAG), evaluation methodologies, and approaches to identifying or mitigating hallucinations.
- Curiosity about emerging developments across AI, recommendation systems, deep learning, and customer personalization, with an ability to translate new techniques into practical business applications.