Schlumberger HR - Federation of Data Science to Accelerate Talent Performance Enablement
Team members: Modhar Khan - Head of People Analytics Richard De Moucheron – Director Total Talent Management Wesley Noah –Global Compliance Managing Counsel Operations Rupinder Kaur – Data Scientist Talent Analytics Sampath Reddy – Analytics Product Champion Vipin Sharma - Technical Leads Analytics Juliette Murray Lamotte – Global Compensation Value Manager Rafael Fejervary – Global Talent Manager Simon Spero (Dataiku) - Senior Enterprise Customer Success Manager)
Country: United States
Description: Schlumberger is a technology company that partners with customers to access energy. Our people, representing over 160 nationalities, are providing leading digital solutions and deploying innovative technologies to enable performance and sustainability for the global energy industry. With expertise in more than 120 countries, we collaborate to create technology that unlocks access to energy for the benefit of all.
AI Democratization & Inclusivity
With superior talent and a vast data warehouse available to Talent Management teams across the globe, the journey towards applying machine learning on the edge was challenged with the following requirements:
Investment in learning and training,
Compliance monitoring and ethical use of data (assurance),
Bringing stakeholders together to discuss and assure the value of such projects.
Furthermore, a challenge on capacity and resourcing also emerged in complex scenarios, in which the talent team across the world needed the technical expertise of the central data science team to support and enable components of talent-specific data projects such as talent planning, acquisition, identification, skilling and retention involving multitudes of unsupervised learning (e.g. clustering), text mining and NLP (e.g. embedding, NLP – identity), and supervised learning (ensemble modeling).
The material provided by Dataiku covered all the needs and catered to various competencies and profiles (e.g. data engineering, analysts, business partners), which reduced our journey to data science at scale by months and years.
The platform enabled connectedness across multiple teams and drove efficiency in projects decisions, as well as visibility on where support was needed. In the past, we had many reviews to get stakeholders to understand what data was used and how engineering was applied, which went on for months. Today, they have instant visibility on the entire data pipeline.
A big challenge was how to ensure that all the projects being done on the edge are compliant to privacy regulations and bias elimination, without stopping creativity. With the clear reporting tools and automation of such reviews, teams are able to work more efficiently at scale - where it would previously take weeks and months to complete such reviews before projects begin.
During the pilot conducted for one month with 10 members from various teams (compensation, talent acquisition) and personas (recruiters, compensation analysts, talent acquisition planners), we saw more than 5 projects being deployed to solve local business needs. We have a plan to expand and add more than 100 HR personnel on the platform in Q3-4 of this year.