Principal Data Scientist – DDSAI GD
About this opportunity
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com .
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Data Analytics & Computational Sciences
Job Sub Function:
Data Science
Job Category:
Scientific/Technology
All Job Posting Locations:
Hyderabad, Andhra Pradesh, India
Job Description:
About Innovative Medicine
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine
Job Summary
J&J Innovative Medicine – Data, Data Science, and AI - Global Development (DDSAI GD) is seeking a Principal Data Scientist to lead end-to-end delivery of integrated analytical solutions for defined clinical-development and trial-operations business problems. This role owns solution definition, delivery, adoption, and use-case outcomes across connected stakeholder groups using ML, GenAI, optimization, and RWD.
Key Responsibilities
Own the end-to-end definition, development, validation, deployment, and adoption of integrated data-science solutions for defined business problems.
Lead predictive modeling, simulations, optimization, and GenAI applications for study feasibility, enrollment forecasting, country and site selection, protocol design, and operational planning.
Apply clinical, operational, cost, and RWD, including EHR, claims, registries, and clinical-trial data, to generate evidence on patient populations, treatment pathways, feasibility, and operational risks.
Lead RWD analytics that inform protocol feasibility, eligibility criteria, recruitment and retention, study design, comparative evidence, and external-control use cases.
Develop LLM-enabled workflows for information extraction, protocol comparison, data harmonization, clinical-concept interpretation, and targeted workflow automation.
Partner with clinical, operational, technology, and data stakeholders to frame questions, manage trade-offs, and translate outputs into adopted recommendations and measurable use-case outcomes.
Apply model-validation, MLOps, documentation, privacy, data-quality, and responsible-AI standards; contribute reusable code, data assets, and technical patterns.
Communicate methods, model outputs, assumptions, limitations, and decisions clearly to technical and nontechnical audiences.
Mentor junior colleagues and strengthen delivery practices across project teams.
Required Qualifications
Master’s degree in computer science, engineering, biostatistics, health economics, biomedical informatics, applied mathematics, or another quantitative discipline; PhD preferred.
At least 5 years of industry experience leading end-to-end delivery of data-science solutions using ML, predictive modeling, optimization, NLP, GenAI, or RWD analytics.
Demonstrated experience defining, validating, deploying, and driving adoption of analytical solutions that inform clinical-development, clinical-operations, or real-world evidence decisions.
Hands-on experience with structured and unstructured clinical, operational, or RWD data, including multimodal modeling and time-series or simulation methods.
Proficiency in Python and SQL, modern ML, optimization, and LLM-development libraries, and reproducible MLOps practices.
Strong communication, stakeholder-management, and technical-leadership skills.
Preferred Qualifications
Experience in healthcare, pharmaceutical development, MedTech, clinical development, or clinical operations.
Experience applying RWD methods to clinical-trial design, including target-trial emulation, propensity methods, survival analysis, or time-to-event analysis.
Experience delivering analytical products or applications used by clinical or business teams.
Required Skills:
Preferred Skills:
Advanced Analytics, Coaching, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Digital Fluency, Econometric Models, Organizing, Process Improvements, Strategic Thinking, Technical Credibility, Workflow Analysis
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 141 comparable Principal Biostatistics & Data Science roles across 30 biopharma companies.
Salary context
55 of 141 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 141 comparable roles
+ 13 more countries
Seniority mix
141 of 141 peers have a known seniority level
Therapeutic area mix
14 of 141 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.