Healthcare DSP uniting media, identity, and clinical data for pharma marketing
DeepIntent operates a demand-side platform tailored to healthcare marketing, combining media buying with identity resolution and clinical data. The stack reveals an engineering organization in transition: heavy use of batch processing (Spark, Airflow, BigQuery, Druid) alongside emerging real-time infrastructure (Kafka adoption), and aggressive AI/automation investment (OpenAI, agentic frameworks). Hiring acceleration in data (5 open roles) and engineering (4) paired with pain points around test coverage, delivery throughput, and administrative automation suggests they're scaling both data science and internal operations in parallel.
DeepIntent is a healthcare demand-side platform serving pharmaceutical brands and their agencies. The platform connects media buying, identity data, and clinical information to support omnichannel campaign planning and optimization. Founded in 2016 and based in New York, the company operates with 201–500 employees across engineering, data science, and commercial teams. Active projects include real-time bidding deployment, outcomes measurement, audience activation expansion, and AI-driven workflow automation. The organization is hiring across data and engineering functions in the United States and India, with current velocity accelerating.
Core stack: Python, Java (Spring Boot), MySQL, GCP/AWS, Kafka, Spark, Airflow, BigQuery, Druid, Beam. Analytics: Looker, Tableau, Data Studio, Power BI. Testing: Selenium, Playwright, Robot Framework. Adopting: Hadoop, Kafka, Airflow for real-time and distributed processing.
Real-time bidding model deployment, AI agent automation of administrative workflows, agentic framework development, audience connectivity expansion, measurement strategy design, and integration of DeepIntent solutions into pharma technology stacks.
DeepIntent's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.