Our customer is a healthcare services organization that develops and delivers “point-of-care” technology to improve care coordination, compliance, and physician-patient interactions.
Founded in 2006, the organization has doubled each year in its scale of technology implementation at outpatient healthcare facilities, business partnerships with global life sciences and medical device companies, product innovation and team growth. The organization’s point-of-care technology platform impacts 500M patient visits annually.
The customer organization employed a ‘proof of play’ auditing process in order to show stakeholders that relevant content was displayed on digital displays of more than 100,000 devices that has been installed in doctor’s offices all over the country. The ‘proof of play’ process leveraged the technology of image processing and EXIF data.
The customer’s Data Engineering and Data Science team was tasked with helping passage this proof-of-play batch process to run in real time. The team quickly decided on the tech stacks of AWS micro-services (Lambda, S3, and RDS) with python as the programming language. Their challenge however was one of staffing. The data science/engineering team had to necessarily scale quickly in order to support the rapidly growing install-base, and finding the right talent proved to be a challenge in light of the customer’s allocated budget and the short supply of experienced professionals in this high-demand area.
In order to address the customer’s challenge, DivIHN designed and implemented an ‘Alternative Talent Solutions’ program that involved augmenting the customer’s existing ‘Data Science / Engineering’ team with a ‘Data Science Pod’ composed of young Data Scientists with a Master’s degree in Data Analytics and 2-3 years of related experience. The starting point involved an intake call with project stakeholders at the client which included the customer’s Vice President for Growth Solutions, the Director of the Data Engineering team, and the head of the Human Resources team, to get a deeper understanding of the organization’s requirements, challenges, culture, budget, and then jointly map out a staffing strategy and execution plan. The plan included a sourcing strategy, assessment process, and onboarding timelines.
The candidates we identified for this engagement were largely sourced through our referral network. Identified candidates were put through an online ‘data analytics’ test that involved live-coding, a Skype-based technical interview with our in-house assessor, and a behavioral skills interview with our client-relationship manager, before being presented to the customer. Since the bulk of the candidate assessments were completed by DivIHN, the customer did not have to spend much time on interviewing candidates, thus allowing them time to focus on their core business.
The team that DivIHN formed for the customer were assigned onsite mentors and a brief on-the-job training program to help them become productive quickly. In addition, DivIHN’s client-relationship manager continued to stay in close touch with the deployed resources in addition to periodic cadences with the project stakeholders to collect feedback on performance and progress.
In view of the customer’s urgency to scale the team as quickly as possible to mitigate serious business impacts, DivIHN presented the first slate of tech-screened candidates to the customer within 72 hours of the initial intake call. Following a final interview by the client project manager, DivIHN speed-tracked the background check process and onboarding to enable the team to be onsite within just 3 weeks of the customer’s reach out to us.