Data Scientist, Senior

The Senior Data Scientist (Strategy Post-Sales Data Science) is a senior individual contributor on Infor's Post-Sales Data Science team. This role is responsible for owning analytical workstreams end to end in close partnership with business stakeholders across various business functions. You will work on high-priority questions related to customer adoption, retention, expansion, and growth, translating strategic business problems into rigorous, actionable analysis. This is a role for someone who combines strong analytical and technical skills with the communication ability and business judgment to create measurable business impact with data-driven insights. A Typical Day in the Life Includes: • Own data science workstreams end to end. Take business questions from initial scoping through data analysis, insight synthesis, and stakeholder communication. Manage day-to-day relationships with business partners. Develop exploratory insights, statistical models, and causal frameworks as appropriate. • Frame problems before solving them. Work with stakeholders to translate business objectives into clear analytical frameworks. Influence problem framing to ensure questions can be answered using data science. • Translate findings into decisions. Develop clear narratives and exec-facing presentations that connect analytical results to business decisions. Prioritize actionability and connection to business outcomes without sacrificing methodological transparency. • Collaborate across team on data foundation and DS practices. Work as part of a cohesive data science pod to share best practices, innovations and business/data knowledge. • Support turning insights into actions. Develop production-ready analytical scripts and contribute to collaborations with IT on deployment of insights into operational workflows. Basic Qualifications: • SaaS product or customer analytics background. Significant experience working on user/customer adoption, retention, engagement, or lifecycle analytics in a B2B SaaS or enterprise software environment. Comfort thinking in terms of cohorts, funnels, and behavioral metrics. • End-to-end analytical ownership. Demonstrated ability to independently own a full analytical project, from problem framing to data acquisition and preparation, analysis, and insight communication, all with minimal technical supervision. • Strong SQL and Python skills. Ability to independently explore and transform data, build analytical datasets, and implement statistical models. Comfort working in Snowflake. • Stakeholder-facing communication. Proven ability to present analytical findings to business stakeholders in a clear, compelling, and understandable way. Experience relating methodology to business narrative and clearly identifying recommended actions. • Statistical rigor. Solid grounding in statistical methods relevant to business analytics: regression modeling, segmentation, A/B testing, survival analysis, propensity modeling. Ability to explain methodological choices clearly and evaluate and articulate tradeoffs between approaches. Preferred Qualifications: • Customer Success domain experience. Prior work supporting or embedded within a Customer Success organization, or direct experience with adoption, churn, and expansion analytics. • Experience influencing business decisions with data. Examples of analytical work that demonstrably changed a business decision or drove a stakeholder to action that measurably impacted KPIs. • Data visualization and storytelling. Strong demonstrated instinct for visual communication of quantitative findings. • Experience with AI data science workflows. Comfort with using generative AI tools to accelerate data science workflows.