Role Description We are looking for a detail-obsessed Product Support Specialist & Data Analyst who thrives at the intersection of product support and data analysis. This role blends hands-on QA/QC of product enhancements, technical documentation, customer support, quantitative analysis, dashboard and reporting design, and research - using both traditional research and AI tools to move faster and more accurately. Key Responsibilities - Quality Assurance & Quality Control (QA/QC) - Build and maintain QA checklists and validation routines that catch errors or configuration drift before they reach the customer. - Investigate data discrepancies, identify root causes, and coordinate fixes with the software engineering team. - Monitor ongoing accounts for anomalies - stale data, broken feeds, or numbers that don't tie out - that could affect data quality or customer trust. - Documentation - Create and maintain clear, repeatable documentation for setup processes, configuration standards, data definitions, and troubleshooting steps. - Keep internal knowledge base current as the product and processes evolve. - Write customer-facing guides and quick-reference materials as needed. - Customer Communication - Serve as a primary point of contact for customer questions - understanding what the customer actually needs and explaining the answer clearly. - Know when and how to escalate an issue, and keep the customer informed. - Translate customer-reported issues into clear, reproducible write-ups for software engineers to investigate and fix. - Customer Setup & Expansion - Help with new customer onboarding: Configure accounts, setup data, validate data connections, and confirm setup meets the customer's goals. - Expand and reconfigure existing customer setups as their needs grow - new data, new use cases, additional users, or additional capabilities. - Partner with Sales to translate customer requirements into technical configuration. - Quantitative Analysis - Apply statistical methods, regression analysis, probability analysis, and simulation - to understand trends, validate data, and explain what the numbers mean. - Use quantitative analysis to sanity-check outputs, identify outliers, and distinguish a real issue from normal variability. - Translate analytical findings into plain-language explanations customers and internal teams can act on. - Data Visualization & Reporting - Design and build dashboards, charts, and reports that turn large or complex datasets into clear, actionable information for managers, executives, and users. - Tailor visualizations and reporting cadence to the audience - including executive summaries and detailed reports. - Ensure dashboards and reports remain accurate as underlying data or configurations change. - Research - Research relevant data, industry benchmarks, and context to support customer questions and internal decision-making. - Use AI tools to accelerate research, summarize findings, draft documentation, and support QA and analytical workflows. - Stay current on relevant data sources, tools, and best practices. Qualifications - 5+ years in customer support, technical support, implementation, or account management, ideally with enterprise/B2B software. - Experience in direct interactions with customers via calls and/or email - fielding questions and issues, explaining answers clearly, and escalating when needed. - Ability to translate a customer-reported issue into a clear, reproducible write-up that a software engineer can understand and address. - Strong attention to detail and comfort with on-going QA/QC work of product functionality and new enhancements. - Resourcefulness - comfortable figuring out an unfamiliar problem, tracking down an answer, and finding a workable path forward without a playbook. - Ability to work independently - can take ownership of a task or account and drive it to completion with minimal oversight, while knowing when to loop others in. - Working knowledge of quantitative/statistical analysis including regression analysis, probability analysis, simulation, and able to apply them to real data. - Demonstrated ability to turn large or messy datasets into clear dashboards, charts, or reports for executives, managers, and users. - Experience writing clear technical and process documentation. - Comfort working in spreadsheets and dashboard/BI tools (e.g., Excel, Tableau, Power BI, or similar). - Strong written and verbal communication skills; able to explain technical and quantitative concepts to non-technical audiences. - Experience using AI tools (e.g., ChatGPT, Claude) to support research, writing, analysis, or QA work. Preferred - Background or coursework in statistics, data science, applied math, economics, or a related quantitative field. - Experience with onboarding/implementation for enterprise software customers. - Experience building dashboards and data visualizations from scratch including reports & charts design. - Experience with tools and/or data sets that manage large data sets and complex data structures. - Familiarity with SQL or basic data querying and extraction tools.