Data Scientist and Application Developer

Johnson & Johnson Johnson & Johnson · Pharma · Titusville, NJ +1

This role involves developing data-driven solutions for manufacturing, alternating between building internal web applications for analytics and operationalizing models for product quality decisions in a GMP context. Responsibilities include application development using Python (Flask/FastAPI), data science fundamentals for manufacturing datasets, model development with a focus on interpretability, and collaboration in a regulated environment.

What you'd actually do

  1. Build and maintain internal web applications that make analytics usable in day-to-day workflows.
  2. Develop services using Python with Flask (and/or FastAPI where appropriate), including: Lightweight APIs for data access and model execution User-facing interfaces using Flask + Jinja, with small amounts of JavaScript and optionally React for richer interactions.
  3. Implement pragmatic software engineering practices (modular design, testing, logging/monitoring hooks, clear documentation) aligned to intended use (GMP vs non-GMP).
  4. Apply strong fundamentals in statistics, data extraction + cleaning, and modeling to diverse manufacturing datasets (e.g., batch/process time series, quality data, laboratory and instrument outputs).
  5. Develop, evaluate, and document models intended to support insights into manufacturing performance in a GMP environment, focusing on interpretability and lifecycle considerations.

Skills

Required

  • Bachelor’s degree (or higher) in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent practical experience).
  • Strong data science fundamentals: Basic statistics and experimental reasoning Data wrangling and exploratory analysis Model building and evaluation.
  • Strong Python skills for both analysis and application development.
  • Familiarity with basic SQL fundamentals is required.
  • Experience building user-facing applications using Flask (templates/Jinja) and familiarity with basic front-end concepts (HTML/CSS/JavaScript).
  • Comfort working with diverse, imperfect datasets and iterating toward robust solutions.
  • Strong collaboration and communication skills in a cross-functional environment.

Nice to have

  • Familiarity with FastAPI and API design concepts (can be learned on the job if needed).
  • Experience supporting software or analytics in regulated/GMP-like environments (validation mindset, documentation discipline, traceability).
  • Exposure to manufacturing, pharma, chemical, or other industrial process data.
  • Knowledge of Azure services and/or identity concepts (e.g., Entra ID) is a plus but not required.
  • Familiarity with containerization/DevOps practices (e.g., Docker, CI/CD) is helpful.

What the JD emphasized

  • GMP context
  • GMP environment
  • regulated
  • auditable

Other signals

  • operationalizing models
  • product quality decisions
  • GMP context
  • manufacturing datasets
  • interpretability
  • lifecycle considerations