ServiceNow's Customer Service & Support (CSS) team is looking to operationalize predictive models, design rigorous experiments, and translate insights into clear, actionable recommendations for executives to treat data as a critical business asset—reliable, secure, compliant, and readily available to drive decision-making, innovation, and growth.
Requirements
- Strong skills in SQL and Python with hands-on experience in experimentation design and analysis. ; ability to design, build, and productionize models and pipelines.
- Experience with support analytics (examples: backlog/SLA/shrinkage and productivity analytics, measuring AI impact (assisted vs autonomous), text analytics on case notes and KBs, workforce and capacity planning tie-ins, CSAT/NPS and sentiment linkage, cost-to-support modeling)
- Strong grounding in statistical modeling, experimental design, and causal inference methods.
- Experience with data visualization and storytelling tools (e.g., Tableau, Power BI, Plotly, or equivalent).
- Familiarity with cloud-based data platforms (e.g., Snowflake, Databricks, AWS, GCP, or Azure)
- 6+ years of experience in data science (forecasting, causal inference, uplift modeling).
Responsibilities
- Develop and maintain forecasts (volume, TTRF) and uplift/propensity models for deflection and containment.
- Design and analyze A/B and holdout tests across portal, IRP, and NAVA; quantify incremental impact.
- Build driver analyses and scenario models that tie directly to program decisions and investments.
- Ship production-ready features and pipelines in SQL/Python (with lightweight dbt where needed).
- Document and monitor model risk and Responsible AI considerations.
- Continuously evaluate and improve model performance, ensuring accuracy, fairness, and business relevance.
- Establish and maintain data pipelines, monitoring, and reporting frameworks to ensure insights are timely, reliable, and actionable.
Other
- Translate data-driven findings into compelling executive recommendations that influence strategy and resourcing.
- Partner cross-functionally to drive business outcomes
- Champion a data-driven culture within CSS by coaching peers, enabling self-service analytics, and sharing best practices.
- Track and communicate emerging trends in predictive analytics, AI/ML, and experimentation; recommend adoption where impactful.
- Proven ability to translate complex findings into clear executive-level storytelling.
