California-based ServiceNow, one of the leading names when it comes to operating as a cloud-based company delivering Software as a Service (SaaS) has brought purpose-built AI with the highly intelligent NOW platform. Last September, the company expanded this using a domain-specific ServiceNow language model designed for enterprise use.
The NOW platform converts machine intelligence into practical actions, aiming to enhance process efficiency, reduce risks, optimise workforce productivity, and facilitate automated workflows with the help of purpose-built AI, providing users with self-solving capabilities through augmented intelligence
“With this (NOW Platform), we are enabling enterprises to increase process efficiency, minimise risk by avoiding human mistakes, optimise workforce productivity to focus on higher value tasks, leverage automated workflows to drive standardisation and empower users to self-solve with augmented intelligence,” Sumeet Mathur, vice president and managing director of ServiceNow’s India Technology and Business Center, told AIM.
The company has eight open positions in data science.
Applied research scientists in the Core LLM Team focus on developing generative AI solutions, collaborating with diverse teams to create AI-powered products and work experiences. Simultaneously, they conduct research, experiment, and mitigate risks associated with AI technologies to unlock novel work experiences.
On the other hand, as a machine learning engineer, you’ll craft user-friendly AI/ML solutions to enhance enterprise services’ efficiency, emphasising accessibility for users with varying technical knowledge.
Inside ServiceNow’s AI & Analytics Lab
The Now platform aims to create proactive and intelligent IT processes. The platform is built around big data and advanced analytics, incorporating real-time and stored data to enhance accessibility and support various use cases, such as self-service, incident detection, pattern discovery, knowledge base optimisation, workflow automation, and user empowerment.
ServiceNow’s self-service has evolved with augmented AI and automation, using intelligent virtual agents to understand customer intent and resolve complex issues. Augmented agent support focuses on improving human capabilities through recommendation engines, automated workflows, and increased productivity, aligning with specific business objectives for measurable value.
Tapping into Generative AI
Last September, the company expanded its Now Platform using a domain-specific ServiceNow language model designed for enterprise use, prioritising accuracy and data privacy. The Now LLM incorporates top-notch foundational models, including a pre-trained model called StarCodel, developed in collaboration with Hugging Face and a partnership with NVIDIA, along with other open-source models.
The initial release of Now LLM introduces features such as interactive Q&A, summarisation capabilities for incidents/cases and chats, and assistive code generation for developers. The development of this model involved significant efforts from engineering, research, product, QE, and design teams, as well as data centre operation teams managing the GPU infrastructure.
Clients like Mondalez, Delta, Standard Chartered, Coca Cola, LTIMindtree, and various other companies across industries have used the platform for AI applications in areas like improving healthcare workflows, providing financial auditors with quick insights, and transforming supply chain management in manufacturing.
“We believe that the most constructive and value-creating strategies for generative AI are grounded in embedding human experience and expertise into its core capabilities,” added Mathur.
So it adopts a humans-in-the-loop model for generative AI, integrating human expertise into its core capabilities. The NOW platform’s generative AI is applied in diverse use cases, including case summarisation, content generation, conversational exchanges, and code generation.
Interview Process
“Our hiring process for data science roles follows a structured approach aimed at attracting a diverse pool of qualified candidates. We publish job openings on various platforms, including our career site, job boards, social media, and professional networks,” added Mathur. The process involves careful evaluation through interviews to ensure the selection of the right candidate.
The interview process consists of three technical rounds, each focusing on key competencies such as programming proficiency and experience with core ML and LLM. This assessment is followed by an interview with the hiring manager and, for certain roles, an additional round with the senior leadership.
However, Mathur shared that during the data science interview process, candidates often make common mistakes that should be avoided. Some of them include inadequate technical readiness, a limited understanding of the company’s objectives and role, failure to ask insightful questions, overlooking the latest AI/ML trends, and neglecting to demonstrate effective problem-solving skills.
Expectations
Upon joining the data science team at the Advanced Technology Group (ATG) of ServiceNow, candidates can expect to work within a customer-focused innovation group. The team builds intelligent software and smart user experiences using advanced technologies to deliver industry-leading work experiences for customers.
The ATG comprises researchers, applied scientists, engineers, and product managers with a dual mission: building and evolving the AI platform and collaborating with other teams to create AI-powered products and work experiences. The company expects that team members will contribute to laying the foundations, conducting research, experimenting, and de-risking AI technologies for future work experiences.
Work Culture
“Our company fosters a purpose-driven work culture where employees have the opportunity to be part of something big. We make work better for everyone—including our own. We know that your best work happens when you live your best life and share your unique talents, so we do everything we can to make that possible for our employees,” Mathur added.
Some of the key perks include a hybrid working model, paid time off, well-being days, employee belonging groups, DEI learnings, internal opportunities, and paid volunteering.
According to him, joining ServiceNow means becoming part of an inclusive and diverse community with resources for well-being, mental health, and family planning, among others. Prioritising value and trust, SaaS giant provides ongoing support for learning and development, growth pathways, and action-oriented feedback aligned with clear expectations. The programs cater to individuals at all career stages.
“We’re committed to creating a positive impact on the world, building innovative technology in service of people – with a core set of values and a deep responsibility to each other, our customers and our global communities,” he concluded.
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