Teams at ZS

Team

Transforming Global Healthcare and Beyond

Today, having a wealth of data on hand or digital literacy isn’t enough to cement an organization’s success — it needs advanced insights and custom tech that can spot inefficiencies, drive decision-making and power innovation. At ZS, the global management consulting and technology firm is leveraging proprietary technology — chief among it, the company’s ZAIDYN platform — to help bring healthcare clients into a new era. In an industry that constantly seeks clear solutions for complex problems, team members at ZS know that their work can positively impact the future of healthcare: “There is nothing more satisfying than seeing your product improving efficiency,” said Associate Principal of Digital & Technology, Laura Elisa Montealegre.

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Search the 23 jobs at ZS

Recently posted jobs

8 Hours Ago
Boston
Hybrid
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
Sr. Lead, Customer Success role at ZS's Beyond Healthcare Analytics Team involves owning end-to-end delivery on aligned objectives with clients, building required storyboards with insights/impact summary, delivering client impact, translating client requirements to data science teams, and finding data-driven insights to solve important business problems.
20 Hours Ago
Boston
Hybrid
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
The Quantitative Market Research Methodologist at ZS partners with client teams to execute complex quantitative market research projects. Responsibilities include study design, statistical modeling, data analysis, and mentorship of junior team members.
3 Days Ago
Boston
Hybrid
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
ZS is looking to hire NLP Engineers to bring digitization to the clinical development ecosystem. The NLP Engineer will use cutting edge technology to mine historical trial related documents and build a structured graph-based dataset. Responsibilities include building model pipelines, scaling NLP algorithms, enhancing the NLP platform, implementing ML Ops, and writing production-ready code.