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 workplace. 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.

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.

The Senior Data Scientist (Strategy Post-Sales Data Science) is a senior individual contributor on Infors 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.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Scientist, Senior (Hyderabad)
🏢 Infor
📍 Hyderabad