Applied AI Scientist - Commercial

Amneal

19h

About The Position

The Applied AI Scientist - Commercial builds and deploys machine learning and agentic AI systems that improve pricing, contracting, customer, and portfolio decisions across Amneal's generics and specialty businesses. This is a hands-on technical role accountable for production systems used daily by Commercial Operations, Pricing & Contracting, Marketing, and Market Access, with business outcomes baselined and validated by Finance. The role sits within the Enterprise AI Transformation function.

Requirements

Bachelors Degree (BA/BS) Computer Science, Statistics, Economics, Operations Research, or related quantitative field (with equivalent demonstrated experience)
5 years or more in Developing and deploying machine learning models in a production environment
5 years or more in Advanced proficiency with Python and SQL, including experience with PySpark or comparable distributed processing
5 years or more in Demonstrated application of forecasting, pricing, or econometric methods to commercial problems
5 years or more in Pharmaceutical or life sciences commercial analytics
Working knowledge of Agentic LLM application patterns including retrieval-augmented generation, structured extraction, and evaluation methodology (required)
Track record of working directly with business stakeholders to define problems, agree success measures, and drive adoption (required)

Nice To Haves

Master's Degree (MS/MA) Computer Science, Statistics, Economics, Operations Research, or related quantitative field
Ph. D. Computer Science, Statistics, Economics, Operations Research, or related quantitative field
Generics market dynamics: gross-to-net accounting, chargebacks, rebates, GPO and wholesaler contracting, price erosion modeling (preferred)
Specialty and branded commercial analytics: payer and market access data, prescriber-level targeting, patient services data (preferred)
Third party pharmaceutical data assets - IQVIA, Symphony, or equivalent (preferred)
Databricks (Unity Catalog, Delta Lake, MLflow) and cloud ML platforms such as Amazon Bedrock or SageMaker (preferred)
Causal inference methods for observational data (preferred)

Responsibilities

Develop predictive and optimization models with LLM as interface supporting customer bid and tender decisions, including win-probability modeling and price-volume trade-off analysis across the generics portfolio.
Build revenue and volume forecasting models at NDC and brand level accounting for competitive entrants, loss of exclusivity, price erosion curves, and customer concentration.
Analyze gross-to-net performance, chargeback, and rebate data to identify and quantify margin leakage, and implement recurring detection rather than one-time analysis.
Model customer and channel profitability across wholesalers, GPOs, retail, and specialty accounts to inform contracting strategy and account prioritization.
Instrument deployed models with monitoring for drift, data quality, and business KPI degradation, and own remediation.
Establish benefit baselines with business owners, partner with Finance to validate realized impact, and document methodology to a standard supporting internal audit and SOX review where financially material.
Present findings, recommendations, and model limitations to Commercial leadership.
Build analytics supporting specialty product launches, including patient and prescriber dynamics, payer coverage and access barriers, and field targeting.
Develop portfolio and business development models assessing new product opportunity: market size, expected entrant count, erosion trajectory, and risk-adjusted return.
Build LLM-based and agentic applications for commercial workflows, including contract, tender, and market intelligence document extraction, supported by formal evaluation sets and regression testing.