Data Scientist, Decision Support

Company Description

It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

At ServiceNow, we make the world work better for everyone. Our global Customer Service & Support (CSS) team is at the heart of that mission—ensuring our customers have seamless, efficient, and exceptional experiences.

We are looking for a Decision Scientist, Enterprise Data Management & Continuous Improvement to operationalize predictive models, design rigorous experiments, and translate insights into clear, actionable recommendations for executives. This role will help us treat data as a critical business asset—reliable, secure, compliant, and readily available to drive decision-making, innovation, and growth.

About the Role

As a Decision Scientist, you will develop, test, and scale predictive models that improve support outcomes (e.g., case volume, time-to-resolution, deflection, and containment) while maximizing capacity. You’ll design experiments, create forecasts, and deliver insights that directly shape strategy, investments, and roadmaps across CSS.

Key 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.
• Partner cross-functionally to drive business outcomes
• Translate data-driven findings into compelling executive recommendations that influence strategy and resourcing.
• 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.
• 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.
• Support compliance, privacy, and governance requirements in all modeling and experimentation practices.

Qualifications

To be successful in this role you have:

• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
• 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)
• Familiarity with ServiceNow data structures a plus
• Demonstrated experience with AI
• Proven ability to translate complex findings into clear executive-level storytelling.
• Familiarity with B2B, Customer Success or Support
• Strong grounding in statistical modeling, experimental design, and causal inference methods.
• Proven ability to translate complex analyses into clear recommendations.
• 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)

Nice to have: familiarity with MLOps basics (MLflow), RAG/semantic sear