Senior Data Scientist-AI Innovator

Job Description

We’re not yesterday’s IT department, we're Digital Technology. The world around us keeps changing and so do we. We’re redefining what it means to be IT with a mindset centered on transformation, experience, AI-driven automation, innovation, and growth.

We’re all about delivering delightful, secure customer and employee experiences that accelerate ServiceNow’s journey to become the defining enterprise software company of the 21st century.

And we love co-creating, using, and highlighting our own products to do it.

Ultimately, we strive to make the world work better for our employees and customers when you work in ServiceNow Digital Technology, you work for them.

We’re shaping the future of work by leveraging cutting-edge AI and Generative AI technologies. Our team is dedicated to transforming how work gets done by applying new techniques, tools, and data to solve problems that haven’t even been recognized yet.

We rapidly prototype AI-driven solutions, sharing use cases across the company to demonstrate their potential value. As a Data Scientist specializing in Transformational AI, you’ll help bring these prototypes to life through data preparation, coding, and integrations before handing them off to our implementation team.

AI Tools Evaluation : Continuously research, evaluate, and experiment with the latest AI, AutoML, automated feature engineering, and generative AI tools and methods.

Assess their applicability to solve business problems and deliver value.

Adaptation to Novel Use Cases : Adapt new AI technologies and processes to develop tailored solutions that address unique and complex business challenges.

Design data science experiments to validate the applicability of these technologies.

Prototype Development : Rapidly develop and deploy prototypes that showcase the capabilities of novel AI tools, processes and use cases.

Use tools like Gradio and Streamlit to create interactive demos that clearly communicate potential business impacts to stakeholders.

Guide the development of AI / GenAI simulators that help explain model input, output, and results.

Data Set Creation & Preparation : Collaborate with data engineers to identify, design, gather, and prepare MVP datasets that facilitate AI experimentation and MVP model construction.

Automated Feature Engineering : Explore and implement automated feature engineering techniques to accelerate feature extraction and transformation for machine learning models.

Leverage generative AI for feature ideation and hypothesis testing.

Cross-Functional Communication : Present your findings and prototypes to stakeholders, providing clear, actionable insights that demonstrate the value and potential of novel AI technologies.

Foster collaboration between product managers, engineers, and other data scientists to integrate AI innovations into core business processes, driving continuous innovation and learning.

Expand AI Expertise : Maintain deep knowledge of in evolving AI technologies, tools, and methods, ensuring that the company is aware of the changing landscape.

Conduct seminars to explain the new art-of-the-possible.