V
Sr. Data Scientist
V
Sr. Data Scientist
ValGenesisThis job is no longer accepting applications
Job Description
Requirements
- Develop statistical and machine learning models for QbD, CPV, PAT, and process monitoring use cases, including CQA modeling, anomaly detection, and multivariate process analysis.
- Advise product managers and product leaders on how advanced analytics and modeling can create value in the product suite.
- Collaborate cross-functionally with engineering and domain experts to ensure models are technically sound, explainable, and production-ready.
- Create prototypes and proofs-of-concept for new analytics-based product features.
- Ensure modeling practices comply with regulated environments (GxP, GMP).
- Document modeling approaches, assumptions, limitations, and usage patterns clearly for internal and external stakeholders.
- Contribute to product discovery by identifying opportunities where AI, ML, or statistical methods can improve usability, decision-making, or automation.
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About Company
In 2005, we disrupted the life sciences industry by introducing the world’s first digital validation lifecycle management system. ValGenesis VLMS® revolutionized compliance-based corporate validation activities and has remained the industry standard.
Today, we continue to push the boundaries of innovation enhancing and expanding our portfolio beyond validation with an end-to-end digital transformation platform. We combine our purpose-built systems with world-class consulting services to help every facet of GxP meet evolving regulations and quality expectations.
Skills:
Machine Learning, Tensorflow, Nlp, Pytorch, XGBoost, Python, AWS, Sql, Gcp, Azure, vector search, validation frameworks, HuggingFace, MLflow, Pinecone, OpenSearch, LangChain, Generative AI, RAG architecture, LLMs, CI CD for ML deployments, LightGBM, Scikit-learn, prompt engineering, OpenAI LLM APIs, FAISS, Weaviate, LlamaIndex, Model evaluation
Skills:
Machine Learning, Tensorflow, Nlp, Pytorch, XGBoost, Python, AWS, Sql, Gcp, Azure, vector search, embeddings, validation frameworks, HuggingFace, MLflow, Pinecone, OpenSearch, model evaluation, LangChain, Generative AI, RAG architecture, LLMs, CI CD for ML deployments, LightGBM, Scikit-learn, prompt engineering, OpenAI LLM APIs, FAISS, Weaviate, LlamaIndex