Jobs at NPS Prism

Manage a portfolio of strategic clients using NPS Prism to deliver CX insights, drive loyalty strategies, support Bain case teams, grow accounts, lead client engagements, and use AI/workflow automation to accelerate insight delivery.

Lead end-to-end product development for analytics- and research-based (AI-enabled) customer insights products. Define strategy, roadmap, requirements, and success metrics; prioritize opportunities using customer insight and data; collaborate with engineering, data science, commercial, and operations to launch and iterate products.

Own product operations workstreams: build client-ready Tableau dashboards, manage and analyze survey data using SQL/Alteryx/Databricks, design benchmarking surveys, support subscription customers, mentor juniors, and assist commercialization with data-driven proposals and demos.

The Data Engineer will develop and optimize data pipelines, work with cloud platforms, manage data solutions, and collaborate with teams to fulfill business data requirements.

Serve as a client-facing insights specialist: analyze large CX datasets, build and present actionable insights, support onboarding and adoption of the Prism platform, manage client requests and data refreshes, and deliver training and strategic materials to drive client value.

The Product Manager will drive the definition and delivery of analytics and research-based products, focusing on customer needs and collaboration across teams.

The Associate, Product Operations develops visualization dashboards using Tableau, manages data workflows, automates processes, and supports client interaction through data insights.

Own end-to-end technical recruiting for NPS Prism India Hub, partnering with hiring managers to source, screen, interview, and close candidates for data, engineering, and operations roles. Build talent pipelines, implement sourcing strategies, manage offers and onboarding momentum, provide market intelligence and DE&I hiring practices, and report on hiring metrics to improve time-to-fill and quality of hire.

Lead and grow a data engineering team to build, operate, and maintain reliable, scalable cloud data platforms and production data pipelines; drive Agile delivery, cross-functional stakeholder alignment, data governance, and operational excellence.

Collaborate with Product Specialists and Managers to build and maintain automated data pipelines, databases, and visualization dashboards. Analyze large datasets to generate insights, support product launches, survey programming, and stakeholder requests. Ensure quality control, develop analytical queries and workflows, and communicate findings to cross-functional teams to support customer adoption and product improvements.

Manage workstreams to develop visual dashboards, process data with Databricks, and automate workflows. Mentor junior members and enhance team efficiency.

The Analyst, Product Operations will develop data pipelines, work with ETL/ELT workflows, support automation, and prepare data visualizations, collaborating closely with senior team members to ensure quality outputs.

The Senior Data Scientist at NPS Prism will develop and deploy AI solutions, build predictive models, and analyze data to inform business decisions, ensuring production readiness and collaborating with cross-functional teams.

The Staff Data Engineer will design and optimize data architectures and pipelines, mentor staff, and collaborate on data strategy while ensuring quality and governance.

Execute product delivery tasks including survey monitoring, data flow oversight, dashboard reviews, and model validation. Produce analyses and insights, support product feature development, ensure zero-defect delivery, respond to client data/methodology questions, drive process improvements and automation, and collaborate cross-functionally with Operations, Commercial, and Tech teams.

The Data Scientist II position involves developing and deploying AI solutions, building predictive models, and managing data science projects. Responsibilities include collaboration with cross-functional teams, applying statistical analysis, and ensuring model governance. Candidates should have experience in classical ML and modern AI systems with end-to-end project management skills.