Senior Data Scientist

Johnson & Johnson Johnson & Johnson · Pharma · Bangalore, Karnataka, India +1

Senior Data Scientist role focused on building and operationalizing predictive and prescriptive models for intelligent HR workflows at global scale. The role involves creating model-driven solutions for both humans and machines, embedding intelligence into products and processes, and ensuring models are deployed, monitored, and continuously improved in production. Emphasis on responsible AI, explainability, and integrating models into automated or augmented decision workflows.

What you'd actually do

  1. Develop and deploy predictive models that generate signals, features, and recommendations consumed directly by intelligent systems and decision workflows, as well as by humans through explainable, experience‑led interfaces.
  2. Enable end‑to‑end intelligent workflows by embedding predictive intelligence into products, platforms, and processes—ensuring models are deployed, monitored, and continuously improved in production rather than delivered as standalone analyses.
  3. Contribute to model‑driven data products, contributing to how intelligence is created, validated, governed, and consumed across both human‑led and machine‑led decision paths.
  4. Partner with product, engineering, and experience teams to ensure human‑facing experiences and machine‑facing interfaces (signals, features, APIs) are designed together as a single, coherent system.
  5. Adhere to enterprise standards and practices for model explainability, fairness, privacy, and responsible AI, ensuring predictive solutions are trustworthy, auditable, and appropriate for use at global scale.

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field
  • 3-5 years of experience designing, building, and deploying predictive or machine-learning models in production environments, where outputs are consumed by systems, workflows, applications, and end users
  • Proficiency in modern data science and ML tooling (e.g., Python, ML frameworks, feature engineering, model evaluation), with a clear understanding of how models move from experimentation to operational use
  • Demonstrated ability to think in terms of signals, features, and decision logic, and to design models that integrate cleanly into automated or augmented decision workflows
  • Exposure to working on complex, scaled data environments, collaborating closely with engineering, product, and platform teams to deliver durable, production-ready solutions
  • Working knowledge of model explainability, fairness, privacy, and responsible AI practices, and the discipline to apply enterprise standards consistently when building and deploying models

Nice to have

  • Experience working within the Databricks data science and analytics platform, including devel

What the JD emphasized

  • building and deploying predictive or machine-learning models in production environments
  • models are deployed, monitored, and continuously improved in production
  • model explainability, fairness, privacy, and responsible AI practices

Other signals

  • build and operationalize predictive and prescriptive models
  • AI-enabled decision engines and automation
  • move beyond insight delivery to directly shaping how decisions are made, automated, and continuously improved
  • embedding predictive intelligence into products, platforms, and processes
  • models are deployed, monitored, and continuously improved in production
  • human-facing experiences and machine-facing interfaces (signals, features, APIs) are designed together as a single, coherent system
  • responsible AI
  • translate scoped business problems into scalable analytical and modeling frameworks
  • guiding when decisions should be automated, augmented, or explicitly human-controlled