About the Role
Global Commercial Services (GCS) is a leader in providing payments solutions for Small, Medium, and Large Enterprises. The Sales Enablement team is crucial for accelerating sales and driving profitable charge volume growth. The Senior Manager will play a critical role in supporting the development of growth and data-driven strategies to improve Sales portfolio performance and drive execution, working closely with senior leadership within GCS. The ideal candidate should demonstrate creativity, curiosity, and passion for dealing with large amounts of data, converting it into valuable, actionable information. We seek a thought-leader and a problem-solver who can blend business, technical, and industry best practices when developing data-driven solutions.
This position will provide consultative support to the GCS Sales leadership team through the development of analytical solutions. The incumbent will highlight trends, risks, and opportunities to enhance business decision-making processes, working very closely with Sales, Marketing, Finance, Technology, and Capabilities partners to drive Sales growth. The incumbent will lead a team of data scientists to design, develop, and test ML/AI-derived solutions that deliver higher client engagement and efficiency improvements. The role is positioned at a unique intersection of deepening analytics and informing business outcomes, in a fast-paced environment requiring a mix of strong relationship skills, passion for applied data science, people leadership, and a singular focus on excellence.
Responsibilities
- Lead Data Science Projects: Design, develop, and deploy predictive and explanatory analytical solutions that address critical business problems using machine learning, NLP, and generative AI. Strengthen forecasting, refine incentive plan designs, and identify gaming behaviors.
- Drive Analytics: Deliver high-impact analytics to inform strategy by developing actionable insights into Sales and client behavior. Introduce new approaches to transform complex behavioral data and influence decision-making across the organization.
- GenAI Analytics Use Case Development: Lead key workstreams in the design, development, and operationalization of a GenAI-enabled analytics solution that synthesizes internal performance and external competitive signals into actionable insights, with defined success metrics, ongoing monitoring, and approaches for LLM-based feature generation on unstructured data.
- Develop Modeling Capabilities: Build and evaluate models using modern ML frameworks (e.g., TensorFlow, PyTorch), focusing on scalability, performance, and interpretability.
- People Leadership: Lead a team of high-performing data scientists.
- Collaborate Across Teams: Establish and maintain close relationships with key cross-functional stakeholders to understand business strategies, develop goals, and address opportunities.
- Develop Scalable Solutions: Architect and deploy robust, efficient, and scalable data pipelines and modeling solutions using modern cloud and distributed compute patterns.
- 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 strong 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 findings, recommendations, and results to both technical and non-technical audiences, including executive leadership, through clear reports, visualizations, and presentations, to enable data-driven decision-making.
Minimum Qualifications
- 4-5 years of relevant work experience.
- Bachelor’s degree required, preferably in a quantitative field (e.g., Economics, Finance, Computer Science, Mathematics/ Statistics, Engineering).
- Strong analytical and conceptual thinking acumen, with ability to translate complex, unstructured business problems into quantitative models. Leverage external insights and tools (from academia or other industries) where needed.
- 2+ years’ experience of applying machine learning techniques to real-world business problems, including exposure to production ML and/or GenAI (e.g., LLM prompting, RAG, evaluation).
- Capable of articulating key findings to senior leadership and stakeholders, leveraging insights to influence business decisions.
- Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and experience applying algorithms to real-world business problems.
- High proficiency in Python/SQL is required; experience with Hadoop and Spark is a plus.
- Experience with data querying and distributed analytics tools (e.g., Hive, PySpark, BigQuery) is required.
- Experience in a Big Data environment, including data mining and data processing. Ability to address performance issues and to manipulate both structured and unstructured data.
- Demonstrable experience with data visualization and reporting tools (e.g., matplotlib, seaborn, Tableau).
- Proficiency with industry-recognized ETL methods, processes and standards.
- Advanced knowledge of Microsoft Office Suite (Excel pivot tables, deck-writing).
- Ability to work independently as well as collaboratively in a dynamic, cross-functional environment, with a strong attention to detail and passion for learning.
Preferred Qualifications
- Masters/PhD in a quantitative field (Computer Science, Statistics, Econometrics, Mathematics, Physics, Operation Research, Engineering, etc.).
- Stakeholder management at the executive level.
- People leadership experience.