Data Quality, Data Governance, Sql, Python, analytics best practices, machine learning libraries, data visualisations

software engineering practices , data preparation , model development , Machine Learning, MLops, Python, Sql, Feature Engineering, Ai

statistical concepts , probability , Tableau, Sql, Clustering, Python, Sap Business Objects, R, R-Shiny, Microstrategy, Looker, regressions

Machine Learning, Artificial Intelligence, Scala, Deep Learning, Tensorflow, Pytorch, Spark, Information Retrieval, Python, Sequence Modelling, Representation Learning, User Modelling, Recommendation Systems, Transformers

causal inference , Sql, Python, difference-in-differences, MILP, uplift modelling, constrained optimisation, observational causal methods, optimising promotions, Lagrangian methods, synthetic control, instrumental variables, deep uplift architectures, propensity weighting, marketing incentives, heterogeneous treatment effect estimation, DR-learner, meta-learners, S T X R causal forests, Pricing

Deep Learning, Nlp, Docker, Data Visualization, AWS, Ml, Machine Learning, Gcp, Network Analysis, Azure, PEFT fine-tuning, document understanding, EU AI Act, NIST AI Risk Framework, HuggingFace Transformers, LangChain, Singapore’s Model AI Governance Framework, Predictive Maintenance, Ai, time series modeling, Gen AI, Agentic AI, Cv, RAG, generative AI models, feature engineering, LlamaIndex

constrained optimization , causal inference , Sql, Python, MILP, difference-in-differences, instrumental variables, uplift modeling, synthetic control, propensity weighting, heterogeneous treatment effect estimation, Lagrangian methods