Senior Data Scientist

Sprinklr is the definitive, AI-native platform for Unified Customer Experience Management (Unified-CXM), empowering brands to deliver extraordinary experiences at scale — across every customer touchpoint.

By combining human instinct with the speed and efficiency of AI, Sprinklr helps brands earn trust and loyalty through personalized, seamless, and efficient customer interactions. Sprinklr’s unified platform provides powerful solutions for every customer-facing team — spanning social media management, marketing, advertising, customer feedback, and omnichannel contact center management — enabling enterprises to unify data, break down silos, and act on real-time insights.

Today, 1,900+ enterprises and 60% of the Fortune 100 rely on Sprinklr to help them deliver consistent, trusted customer experiences worldwide.

Job Description Sprinklr has an exciting opportunity for a Senior Data Scientist to join the Enterprise Business Intelligence team. Sprinklr's BI Team is collaborating closely with other teams across the organization to embrace AI and expand its application throughout the enterprise. The Sr. Data Scientist wilil play an important role in this strategic initiative.

This person will work on all facets of the data model, the semantic model, as well as the tools and interfaces they support. They will engineer the underlying data sources, where needed, to support predictive capabilities, natural language modeling, and AI. They will critically evaluate AI solutions and tools, ensuring that AI use cases are supported through responsible, ethical, trusted and compliant approaches.

The Sr. Data Scientist will design Al/ML algorithms to develop predictions, recommendations, data quality improvements and proactive insights for the organization. They will also serve as a Product Manager of the data science and AI model(s), capturing feedback from stakeholders driving a roadmap for the BI Team.

This role requires ongoing collaboration with front-line managers all the way to executive leadership, across all functional pillars of Sprinklr. Therefore, candidates need exceptional communication skills combined with a deep understanding of the business.

This role will take that insight from business processes and customer relationships to further enhance existing models.

Lead and manage the full lifecycle of data science and AI solutions, starting with the engineering of the data model to serve as the foundation.

Develop and deploy predictive models, recommendation models, and other solutions to provide timely insights, recommendations and predictions.

Work closely with engineers, data scientists and analysts within the BI team, and collaborate cross-functionally with other colleagues and stakeholders throughout the organization.

Maintain exceptional understanding of business KPIs and related business/customer processes to ensure the relevancy of the model.

Continuously seek out additional data points that can be leveraged to improve accuracy and applicability of the model’s results.

Work with leaders, product engineers and user experience researchers to understand customer data and explore opportunities to turn qualitative data into quantitative data for use in recommendation models.

Exhibit excellent communication skills needed to articulate the model’s impact in business terms, whether to front-line managers or to executive leaders.

Create and maintain dashboards and interfaces using Power

BI to provide access and interactive capabilities to the model's recommendations and insights.

Leverage and test multiple methodologies for advanced analysis projects (predictive models, clustering / segmentation, etc.), with the intent to select one that yields the most accurate approach.

Use analytical rigor and statistical methods to analyze large amounts of data, extracting actionable insights using advanced statistical techniques such as data analysis, data mining, optimization tools, and machine learning techniques and statistics (e. g., predictive models, LTV, propensity models).Mentor team members on data and analytical projects, or on cross-functional teams.

Requirements:

Masters or PhD in a field that is emphasized in…