Job Description Summary#LI-Hybrid
This role is responsible for building the tools, frameworks, and accelerators that enable the organization to scale AI, ML, and data science capabilities efficiently through automation and reusability. By transforming manual, one-off analytics into automated, production-grade solutions that can be reused across multiple use cases, this leader dramatically increases the speed and consistency of analytics delivery while reducing technical debt and enabling citizen data scientists to leverage enterprise-grade.
This position will be located at the East Hanover site and will not have the ability to be located remotely. This position will require 20% travel as defined by the business (domestic and/ or international).
Job DescriptionKey Responsibilities- Design and implement workflow automation solutions for AI/ML and analytics processes (Airflow, dbt, Azure Data Factory).
- Build CI/CD pipelines for data science code, enabling automated testing, deployment, and monitoring of ML models.
- Design MLOps infrastructure to streamline the model development, validation, and deployment lifecycle.
- Build and maintain a library of reusable AI/ML components (feature engineering modules, model templates, prediction pipelines, visualization templates).
- Create standardized notebooks, scripts, and dashboards for common data science use cases that can be easily adapted for new projects.
- Build tools and platforms that enable business users and citizen data scientists to access AI/ML capabilities without engineering support.
- Implement automated testing frameworks for data science code (unit tests, integration tests, model validation tests, regression tests).
- Work with Infrastructure team to optimize performance and resource utilization of automated AI/ML solutions.
Essential Requirements:- Advanced degree in Computer Science, Data Science, Statistics, or related field; or Bachelor's degree with 8+ years relevant experience.
- 8+ years of experience in analytics engineering, data engineering, or software engineering roles.
- 3+ years of experience leading technical projects or small teams.
- Experience with workflow orchestration tools (Airflow, Prefect, dbt) and CI/CD tools (GitHub Actions, Jenkins).
- Knowledge of MLOps principles and tools (MLflow, Kubeflow, SageMaker).
- Experience with testing frameworks and code quality tools.
- Excellent documentation and communication skills.
Desired Requirements:- Experience in pharmaceutical, healthcare, or regulated industries.
- Background in both analytics/data science and software engineering best practices.
- Creative problem-solver who can identify opportunities for automation and reusability.
- Strong systems thinking with ability to design scalable, maintainable solutions.
- Experience building internal tools and platforms used by diverse user groups including citizen data scientists.
Novartis Compensation Summary:
The salary for this position is expected to range between $145,600 and $270,400 per year.
The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.
Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.
US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.
Salary Range$145,600.00 - $270,400.00
Skills DesiredArtificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai, Organization Awareness, Pandas (Python), Python (Programming Language), R (Programming Language), Sql (Structured Query Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis
