Pandas, Spark, Azure, Sql, Python, AWS, SDLC practices, APIs and services

Structured Query Language, Kubernetes, Python, large language models, Relational Databases, generative AI applications, data analysis techniques, automated build test and deployment pipelines, agent frameworks

Git, Python, Sql, Machine Learning, Gen AI, Jupyter Notebooks, Agentic AI, R

Logistic Regression, Decision Tree, Linear Regression, Svm, scrapy, Machine Learning Algorithms, Sql, Pca, Random Forest, Nosql, Neural Network, Numpy, Pandas, Gcp, Spark, Azure, Python, AWS, scikit-learn, K-mean Clustering, R, scapy, BERT

traceability , cdisc standards , biomarkers , SAS, Data Privacy, Advanced Analytics, Predictive Modeling, Python, Ml, Gxp, Sdtm, Sql, Estimands, Define-XML, Fda, Clinical Trial Design, Longitudinal analysis, Uncertainty communication, drug development, Endpoints, ICH-GCP, R, Quantitative methods, Causal reasoning, EMA, Inspection readiness, Ai, Statistics, ADAM, Data interpretation, Simulation, Survival methods

causal inference , Databricks, Clustering, Time Series Analysis, Azure Synapse Analytics, Azure Machine Learning, Recommender Systems, Hypothesis Testing, uplift modeling, statistical methods, Azure Data Lake Storage, Regression, Classification, dashboard development, lifetime value modeling, personalization algorithms, experimentation design

Data Science, Python, Traditional ML, Research Analysis, Data Designing, embeddings, unstructured datasets, LangChain, Generative AI models, LangGraph, GenAI frameworks, structured datasets, MLflow, Large Language Models, Data Analysis Interpretation, vector databases, Structured Query Language SQL, Regulatory Compliance, DL techniques, Statistical Methods, Weights Biases, text, Machine Learning Engineering Fundamentals, images, Statistical Programming Software, RAG architectures, prompt engineering