Senior Manager, Application Development

Roles and Responsibilities

• Frame ambiguous R&D and regulatory questions as well-posed modeling problems, selecting the appropriate method — classification, sequence modeling, information extraction, retrieval, generation, or statistical inference — and justifying the choice against alternatives.

• Build and evaluate NLP and LLM systems over large corpora of scientific and regulatory text: document parsing and layout-aware extraction, named entity and relation extraction, semantic search and reranking, retrieval-augmented generation, structured output, and multi-step agent orchestration with tool use.

• Develop predictive and statistical models over structured scientific data — analytical method and stability data, formulation and process parameters, historical submission outcomes — including feature engineering, handling of small and imbalanced datasets, uncertainty quantification, and model interpretability.

• Mine historical health authority correspondence (deficiency letters, information requests, complete response letters) to learn recurring deficiency patterns and build submission risk-scoring models.

• Design and own the evaluation discipline for every system shipped: curated golden datasets, inter-annotator agreement, failure taxonomies, offline and online metrics, A/B and regression testing, and quantitative acceptance criteria agreed with the business.

• Build reusable modeling components — embedding and retrieval pipelines, extraction schemas, evaluation harnesses, fine-tuning and prompt-optimization workflows — on Databricks (Unity Catalog, Delta Lake, MLflow, Mosaic AI) and cloud model services (Amazon Bedrock, SageMaker).

• Operationalize models with monitoring for data drift, performance degradation, and cost, and own remediation and periodic retraining.

• Produce audit-quality technical documentation — model cards, data lineage, validation evidence — supporting Amneal's GxP AI validation framework and 21 CFR Part 11 requirements, working with QA on inspection readiness.

• Establish benefit baselines with R&D and Regulatory owners and measure realized cycle-time, accuracy, and capacity outcomes.

• Present methods, results, limitations, and trade-offs to technical and business audiences; mentor junior scientists and engineers on modeling practice.

3. Qualification and Experience

Education

• MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Computational Linguistics, or a related quantitative field; PhD preferred (exception, Bachelors from Tier-1 colleges).

Required Experience

• 5+ years building and deploying machine learning and NLP models in production, with demonstrated ownership from problem framing through monitoring.

• Deep applied knowledge of modern ML and NLP: transformer architectures, embeddings, fine-tuning and parameter-efficient methods, retrieval-augmented generation, structured extraction, and LLM/agent evaluation.

• Strong statistical foundations: experimental design, hypothesis testing, uncertainty quantification, and modeling with small, noisy, or imbalanced datasets.

• Expert-level Python and the scientific ML stack (PyTorch or similar, Hugging Face, scikit-learn, pandas/PySpark); strong SQL.

• Hands-on experience building agentic systems: multi-step orchestration, tool and function calling, and human-in-the-loop evaluation.

• Track record of designing rigorous evaluation frameworks and defending model quality with quantitative evidence.

• Ability to work within documented, controlled processes and produce audit-quality technical documentation.

Preferred Experience

• Document intelligence over long, technical, or scientific documents (OCR, layout parsing, table and figure extraction from legacy PDFs).

• Predictive modeling over chemistry, analytical, or process data; cheminformatics or chemometrics toolchains.

• Experience in a GxP-regulated or other validated environment; familiarity with 21 CFR Part 11, CSV, or GAMP 5 and with AI model validation or governance.

• Exposure to pharmaceutical regulatory content — ANDA/NDA/DMF, eCTD structure, SPL labeling, health authority correspondence. Not required; domain is taught on the job.

• Databricks (Unity Catalog, Delta Lake, MLflow, Mosaic AI) and Amazon Bedrock or SageMaker.

• Publications, open-source contributions, or competition results demonstrating applied ML depth.