About this role
USP’s Digital Product Engineering team seeks a Data Scientist in Hyderabad, India, to develop data-driven solutions for business and scientific problems connected to patient safety and the quality of medicines and supplements. The full-time role focuses on AI and machine learning, particularly Generative AI applications using large language models, retrieval-augmented generation (RAG), and prompt engineering. It has no supervisory responsibilities.
Responsibilities
- Design, develop, evaluate, and deploy AI/ML models. Work with data engineers to access, clean, and prepare large-scale datasets for modeling and experimentation.
- Conduct exploratory data analysis, hypothesis testing, and feature engineering. Use appropriate metrics to improve model accuracy, robustness, and fairness.
- Communicate actionable findings to product, engineering, and business stakeholders. Work with business owners, users, product and program managers, architects, and developers to translate requirements into documented data science solutions.
Required qualifications
- A bachelor’s degree in a relevant field, such as engineering, analytics, data science, computer science, or statistics, or equivalent experience.
- 1–3 years of data science experience with a strong focus on AI, including machine learning, deep learning, and reinforcement learning. Hands-on Generative AI experience with LLMs, RAG, prompt engineering, and vector databases such as FAISS or Pinecone.
- Strong Python, PySpark, and SQL skills; proficiency with ML libraries such as scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers; and experience with visualization tools such as Power BI, Tableau, or Plotly.
- Understanding of model evaluation, interpretability, and production deployment; familiarity with Azure, AWS, or GCP for AI/ML workloads; and the ability to work effectively with stakeholders.
Preferred qualifications include experience with scientific chemistry nomenclature, life sciences, chemistry or other hard sciences, pharmaceutical datasets and nomenclature, or MLOps tools and practices such as MLflow, Kubeflow, and Azure ML. Strong verbal, written, and interpersonal communication is requested. Benefits include company-paid time off, healthcare options, and retirement savings.