Manager, Data Science - Global Finance

Johnson & Johnson Innovative Medicine•New Brunswick, NJ

3d•$117,000 - $201,250•Onsite

About The Position

We are searching for the best talent for Manager, Data Science - Global Finance to be in New Brunswick, NJ. 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. Purpose As a Manager, Data Scientist in the Global Finance Data Science Team, you will develop data science products, including the application of Generative AI techniques, to deliver practical, high‑impact solutions within Global Finance. This role combines advanced data science with software engineering. In addition to developing analytical and machine learning models, you will be responsible for engineering production‑ready applications and integrations that embed insights and automation into Finance and Compliance workflows at enterprise scale. You will work alongside a global team of Data Scientists, Data Engineers, Machine Learning Engineers, and Finance partners to execute the Global Finance AI roadmap, reduce manual effort through automation, and improve decision quality and user experience.

Requirements

Minimum of 6 years of relevant industry experience.
Bachelor’s degree required (preferred fields include Computer Science, Engineering, Data Science, Business Analytics, Finance, or other quantitative/STEM disciplines); Master’s degree preferred.
Proven experience building production applications that operationalize analytics or machine learning (e.g., web applications, backend services, APIs, or workflow tools), beyond exploratory analysis or notebooks.
Hands‑on experience implementing and maintaining Generative AI solutions in production, including: Structured prompting approaches, Integration with enterprise data sources or tools, Validation, guardrails, and quality controls to ensure reliable outputs.
Experience developing AI‑enabled systems that support multi‑step workflows (sometimes referred to as agent‑based or tool‑enabled approaches) rather than single‑response use cases.
Experience deploying, running, and supporting solutions in AWS and/or Azure environments using enterprise services, such as: SageMaker, ECS or EKS, Lambda, API Gateway, Step Functions, CloudWatch / Azure Machine Learning, AKS, App Service, Functions, API Management, Azure Data Factory, Azure Monitor / Application Insights
Strong engineering practices, including version control, documentation, automated workflows, monitoring, and support of production systems.
Demonstrated experience executing Data Science projects from initiation through proof‑of‑concept to production deployment.
Strong foundation in machine learning, statistics, data engineering concepts, and software development.
Experience collaborating with cross‑functional stakeholders, including senior leaders, in regulated or large enterprise environments.

Nice To Haves

Advanced Analytics
Compliance Management
Critical Thinking
Data Analysis
Data Privacy Standards
Data Quality
Data Reporting
Data Savvy
Data Science
Data Visualization
Developing Others
Digital Fluency
Give Feedback
Inclusive Leadership
Leadership
Proactive Behavior
Resource Allocation
Strategic Thinking
Team Management

Responsibilities

Develop end‑to‑end data science solutions, including business problem understanding, data preparation, modeling, visualization, insight generation, and communication to senior stakeholders.
Apply Generative AI methods to enhance analytics and decision support solutions, ensuring outputs are accurate, explainable, and aligned with Legal, Compliance, and IT policies.
Design and maintain structured prompting and configuration strategies to ensure consistent, reliable behavior of Generative AI models in production environments.
Build workflow‑oriented AI solutions that integrate models with enterprise data, tools, and business logic to support multi‑step Finance and Compliance processes.
Write production‑level, scalable code in Python or R and implement machine learning and statistical models for predictive analytics, time series forecasting, decision support, NLP, and GenAI use cases.
Execute projects end‑to‑end, from proof‑of‑concept to full production deployment, including integration with end‑user systems and processes.
Build and automate data processing and modeling pipelines, including orchestration of workflows and deployment of models into production.
Develop and operate user‑facing applications, APIs, or services that surface actionable insights and automation to Finance and Compliance users.
Ensure production solutions meet enterprise requirements for scalability, security, monitoring, documentation, and maintainability.
Partner with Technology, Finance Systems & Data, Compliance, and Data Engineering teams to ensure solutions are adopted, supported, and continuously improved.
Stay current with evolving data science and AI techniques and responsibly apply them to business problems.

Benefits

medical
dental
vision
life insurance
short- and long-term disability
business accident insurance
group legal insurance
consolidated retirement plan (pension)
savings plan (401(k))
long-term incentive program
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
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/foster care of a child
Condolence Leave – 30 days for an immediate family member: 5 days for an extended family member
Caregiver Leave – 10 days
Volunteer Leave – 4 days
Military Spouse Time-Off – 80 hours