Senior Analyst-Data Science (Machine Learning/AI & GenAI)

Job Description

Global Commercial Services (GCS) is the global leader in providing payments solutions for Small, Medium, and Large Enterprises. We are in the business of helping our clients get business done! Accelerating sales and driving profitable charge volume growth are critical for the success of the organization. The Sales Enablement team is instrumental in driving these objectives.

The Senior Analyst will drive the development and execution of data-driven strategies to improve Sales portfolio performance, working closely with senior leadership across Global Commercial Services (GCS). The position focuses on translating complex data into actionable insights that inform decision-making and support growth. The ideal candidate brings strong analytical curiosity and the ability to distill data into clear, scalable, business-relevant recommendations, combining business acumen with technical expertise. This role partners closely with Sales, Marketing, Finance, Technology, and Capabilities teams to identify opportunities, quantify risks, and prioritize high-impact initiatives. Responsibilities include developing analytical frameworks, advising leadership on performance trends, and enabling effective, data-informed decision-making across the organization.

The Senior Analyst will design, develop, and operationalize ML/AI-powered solutions to enhance client engagement and improve channel efficiency, applying advanced techniques across machine learning, NLP, and generative AI to solve business problems and unlock new growth opportunities. This is a business-facing data science role, with success measured by quantifiable impact. The environment is fast-paced and highly collaborative, requiring strong relationship skills, passion for applied data science, and a singular focus on excellence.

Responsibilities

• Lead Data Science Projects: Design, develop, and deploy predictive and explanatory models to address key business problems using machine learning, NLP, and generative AI. Support forecasting, incentive design refinement, and detection of anomalous or gaming behaviors by translating business questions into measurable analytical frameworks
• Deliver Analytics & Insights: Generate actionable insights on Sales and client behavior to inform strategy. Apply rigorous hypothesis testing and maintain reproducible, well-documented analytical workflows
• Support GenAI Use Case Development: Contribute to the design, development, and operationalization of GenAI-enabled analytics solutions that integrate internal performance and external signals. Assist in defining success metrics, monitoring frameworks, and approaches for LLM-based feature generation on unstructured data. Support development of validation approaches (e.g., human-in-the-loop, hallucination detection)
• Build Modeling Capabilities: Develop, evaluate, and iterate models using modern ML frameworks (e.g., TensorFlow, PyTorch), with attention to performance, scalability, and interpretability
• Collaborate Across Teams: Partner with cross-functional stakeholders (Sales, Marketing, Finance, Technology) to understand business needs, refine analytical approaches, and deliver relevant solutions
• Develop Scalable Solutions: Contribute to building and optimizing data pipelines and modeling workflows using cloud-based and distributed computing environments. Support efficient model training, including exposure to GPU and distributed training techniques
• Lead Innovation Through External Perspective: Stay current on advancements in machine learning, deep learning, and generative AI; evaluate emerging approaches; translate theoretical advances into practical, scalable solutions that advance business outcomes. Challenge the status quo and demonstrate curiosity
• Define Performance Indicators: Lead analytics and measurement across key performance indicators. Own stakeholder and executive-level communications on initiative progress, including automated monthly measurement tied to specific strategic initiatives
• Communicate Insights Effectively: Present analytical findings and recommendations to both technical and non-technical audiences, including executive leadership, through clear visualizations, reports, and presentations, to enable data-driven decision-making
  
  

Qualifications

Minimum Qualifications

• 2–3 years of relevant work experience in data science, analytics, or a related quantitative field
• Bachelor’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering, Economics, or related discipline)
• Strong analytical and problem-solving skills, with the ability to translate complex, unstructured business problems into quantitative frameworks and models
• Ability to communicate analytical findings and recommendations clearly to technical and non-technical stakeholders
• Experience applying machine learning techniques using standard libraries (e.g., scikit-learn, TensorFlow, PyTorch) to real-world problems; familiarity with NLP tasks such as text classification, summarization, or information extraction
• Strong proficiency in Python and SQL; familiarity with distributed data processing tools (e.g., Spark) is a plus
• Experience working with cloud-based data platforms (e.g., GCP, AWS, or Azure), including data processing and querying tools (e.g., BigQuery)
• Experience with data visualization tools (e.g., matplotlib, seaborn, Tableau) to communicate insights effectively
• Familiarity with data processing workflows (ETL) and version control (e.g., Git)
• Proficiency in Excel and PowerPoint for analysis and presentation of results
• Ability to work independently and collaboratively in a cross-functional environment, with strong attention to detail
  
  

Preferred Qualifications

• Master’s degree in a quantitative field (e.g., Data Science, Computer Science, Statistics, Mathematics, Engineering, or related discipline)
• Experience with causal inference or experimentation methods is a plus
• Experience applying machine learning techniques in production environments, including exposure to GenAI approaches (e.g., LLM prompting, retrieval-augmented generation, evaluation methods)