Senior Data Scientist - R&D DDSAI - Small Molecule Discovery

Johnson & Johnson Innovative Medicine

About The Position

Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to its Small Molecule Discovery organization. The role sits within the Data Science team and partners with medicinal chemists, automation scientists, data engineers, computational scientists, and technology teams to turn small-molecule discovery data into trusted, reusable, AI-ready assets. The position operates at the intersection of scientific discovery, data science, and scientific data engineering. It shapes how molecular, synthesis and purification, and assay data are represented, connected, and used for scientific decision-making, analytics, machine learning, and emerging AI capabilities throughout the discovery lifecycle. The Senior Data Scientist designs and develops data assets, analytical solutions, and scientific data workflows supporting discovery research across the Design-Make-Test-Analyze cycle. The role works with discovery scientists, automation teams, and technology partners to transform complex scientific data into scalable, well-governed resources and establish foundations for analytics, machine learning, and digital laboratory initiatives. This role is intended to bridge scientific workflows and modern data platforms, improving data quality and enabling insight generation and AI applications across small-molecule discovery. The ideal candidate combines data science capability with a strong understanding of small-molecule discovery data, and can work across scientific and technical teams to develop reusable assets that accelerate discovery outcomes.

Requirements

Master's degree or Ph.D. in Chemistry, Cheminformatics, Data Science, Computer Science, Pharmaceutical Sciences, Chemical Engineering, or a related scientific discipline.
At least 5 years of industry experience in pharmaceutical, biotechnology, or life sciences research environments.
Strong understanding of small-molecule discovery workflows, including molecular structures, reaction data, synthesis workflows, and structure-activity relationships.
Advanced proficiency in Python and SQL.
Experience developing solutions on modern cloud data platforms, including Databricks and Snowflake.
Significant experience working with molecular and reaction data using scientific software libraries and toolkits such as RDKit.
Experience designing analytical workflows and data models for complex scientific datasets.
Understanding of data engineering concepts, including data modeling, metadata management, lineage, reproducibility, and data quality.
Ability to collaborate effectively with scientists, engineers, and technology partners in a matrixed R&D environment.
Strong communication skills and ability to translate between scientific and technical audiences.

Nice To Haves

Experience supporting medicinal chemistry, synthesis, compound registration, or screening workflows.
Familiarity with automated synthesis, laboratory automation, and high-throughput experimentation environments.
Experience with reaction informatics, reaction registration, or scientific workflow platforms.
Experience with OpenEye scientific software.
Experience developing machine learning solutions for molecular, reaction, or experimental datasets.
Familiarity with FAIR data principles and scientific data governance practices.
Experience with graph-based, semantic, or ontology-driven approaches to scientific data integration.
Knowledge of modern software engineering practices, including testing, version control, CI/CD, and cloud-native development.

Responsibilities

Design, develop, and maintain high-quality data assets supporting small-molecule discovery workflows.
Transform molecular, synthesis and purification, and assay data into reusable datasets for scientific decision-making and machine learning.
Partner with scientists and technology teams to define data requirements, business rules, and analytical use cases.
Develop scalable analytical workflows using modern cloud data platforms and scientific computing tools.
Apply data science methodologies to extract insights from complex discovery datasets.
Ensure scientific data are structured, documented, and represented to support advanced analytics and AI applications.
Contribute to data standards, metadata practices, and data quality frameworks for discovery data.
Partner with data engineering teams to ensure discovery data products are scalable, traceable, and reproducible.
Support creation of model-ready datasets for machine learning and scientific AI initiatives.
Evaluate opportunities to apply AI and advanced analytics within discovery workflows.
Serve as a trusted technical partner to medicinal chemists, automation scientists, and in silico discovery teams.
Translate scientific workflows and requirements into computational and data solutions.
Contribute to best practices in scientific computing, data management, and reproducible research.
Mentor colleagues on modern data science approaches and scientific data practices.
Foster collaboration across scientific, technical, and data-focused teams.

Benefits

Company consolidated retirement plan (pension) and savings plan (401(k)), subject to plan terms.
Eligibility to participate in the Company's long-term incentive program.
Vacation: 120 hours per calendar year, subject to policy and date of hire.
Sick time: 40 hours per calendar year, with specified variations for Colorado and Washington residents.
Holiday pay, including floating holidays: 13 days per calendar year.
Work, Personal and Family Time: up to 40 hours per calendar year.
Parental Leave: 480 hours within one year of the birth, adoption, or foster care of a child.
Bereavement Leave: 240 hours for an immediate family member and 40 hours for an extended family member per calendar year.
Caregiver Leave: 80 hours in a 52-week rolling period.
Volunteer Leave: 32 hours per calendar year.
Military Spouse Time-Off: 80 hours per calendar year.