Position Summary

Arcesium seeks an experienced Data Scientist professional to join our Business Operations team within the Client and Partner Development (CPD) organization. The ideal candidate will be comfortable working across large volumes of varied datasets spanning pricing, headcount, market research, commercial performance, and translating that data into the models, frameworks, and insights that drive decision-making. Beyond building models and reporting on data, this person will be expected to draw principled conclusions from their analysis and present clear, strategic recommendations to senior leadership. You will also play an active role in identifying AI-driven solutions that improve how the team works and be a part of AI strategy for CPD. You will collaborate closely with global CPD teams across Sales & Partnership, Revenue Operations, Marketing, Forward Deployed, Account Management and Client Success, Service and Solutions teams to execute complex projects and help the organization scale through data-backed governance.

What you'll do:

• Identify relevant data sources and work with CPD stakeholders across functions to collect, analyze, and interpret large structured and unstructured datasets spanning domains such as pricing, headcount, market research, and operational metrics.
• Devise and apply algorithms and models to extract insight from large data stores. Conduct data and error analysis to improve model performance, and clean and validate data for uniformity and accuracy.
• Analyze data for trends and patterns with clear objectives in mind. Interpret outputs and draw principled conclusions rather than stopping at the analysis itself, ensuring findings inform real decisions.
• Implement analytical models and reporting frameworks end to end, from data ingestion through to leadership-ready outputs.
• Conduct what-if and scenario analyses and translate outputs into clear narratives that are actionable at the senior leadership level.
• Design and develop automation scripts and data pipelines to synthesize and structure data from diverse sources, applying critical thinking to move toward zero-touch processes and maximize end-to-end efficiency.
• Produce KPI dashboards and performance reports using historical data to surface trends and productivity insights for executive audiences, with a focus on telling the story behind the data.
• Act as the primary internal champion for responsible, practical AI use within CPD, educating stakeholders, setting best practices, and ensuring solutions are sustainable and governed.
• Develop reusable AI frameworks, prompt libraries, and automation components that scale adoption across the CPD organization and serve as a foundation for future capabilities.

 

What you'll need:

• Strong verbal and written communication skills, with the ability to present analytical findings clearly to senior leadership and stakeholders across functions.
• 4 to 7 years of experience in a data analytics or business operations role. Prior experience at a financial technology firm is a plus.
• Advanced proficiency in Excel including complex formulas and VBA Macro programming. Strong Excel fundamentals are a core expectation of this role.
• Proficiency in Python or similar scripting languages for data transformation, automation, and analysis.
• Working knowledge of SQL for querying and managing structured datasets.
• Experience with data visualization tools, particularly Power BI, to build dashboards and reports for executive and operational audiences.
• Experience with data mining and statistical analysis, including segmentation techniques and statistical packages for working with large datasets.
• Proven ability to conduct what-if and scenario analysis and communicate implications clearly to non-technical senior stakeholders.
• Experience tracking KPIs and relevant metrics for revenue organizations, aligned with broader organizational performance indicators.
• Demonstrated hands-on experience building AI-powered workflows, automation pipelines, or LLM-assisted tools in a business context.
• Genuine intellectual curiosity about AI and a track record of applying it to real operational problems.
• Ability to balance medium to long-term analytical projects alongside day-to-day operational responsibilities.

 

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