Band
Level 6
Job Description Summary
#LI-Hybrid
This position plays a vital role in integrating AI-driven solutions across various platforms, fostering collaboration between the US commercial team and the India-based data science team. By championing the exploration of cutting-edge AI technologies, the Director ensures that our strategies are informed by the latest advancements in generative AI and large language models (LLMs). This role will collaborate with other members of the team to create, pilot, and scale AI tools to support broader goals to transform business processes and outcomes.
The ideal location for this role is East Hanover, NJ but a distant working arrangement may be possible in certain states. Distant workers are responsible for the cost of home office expenses and periodic travel/lodging will be determined necessary by hiring manager. For associates working on-site, relocation assistance to be within 50 miles of the site may be available. This position will require 10% travel as defined by the business (domestic and or international).
There are 3 positions available.
Job Description
Key Responsibilities:
Experience:
Novartis is seeking an individual with proven experience working with machine learning models. They should have a strong ability to support cross-team development of AI programs. A firm commitment to driving continuous improvement in AI solutions, informed by current innovations, is vital to this role.
Essential Requirements:
Strong understanding of deep learning algorithms, foundational/ LLM models, statistics, and recommendation system
Deep knowledge of Large Language Models (LLMs) such as GPT, BERT, Cohere, and their applications in real-world scenarios.
Novartis Compensation Summary:
The salary for this position is expected to range between $194,600 and $361,400 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.
EEO Statement:
The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.
Accessibility and reasonable accommodations
The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
https://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings
Salary Range
$194,600.00 - $361,400.00
Skills Desired
Artificial Intelligence (AI), Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Mentorship, Stakeholder Engagement, Statistical Analysis, Time Series Analysis
