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.