Nombuso Ntombenhle

I'm a data science professional with a passion for unlocking insights from complex data. My expertise spans causal inference, machine learning operations, Bayesian modeling, and scalable data pipelines. With a strong foundation in statistics and programming, I design and deploy data-driven solutions that drive business impact.

My experience includes working with cross-functional teams to develop predictive models, design experiments, and optimize processes. I've applied causal inference techniques to estimate treatment effects, built and deployed ML models using MLOps pipelines, and leveraged Bayesian methods to quantify uncertainty.

I'm proficient in Python, R, and SQL, and have experience with popular ML frameworks and cloud platforms. I'm excited to apply my skills to real-world problems and continue learning in the ever-evolving field of data science.

Some of my key skills include:

• Causal Inference: DAGs, matching, weighting, DiD, IVs

• ML Systems: fairness, reliability, uncertainty calibration, MLOps

• Bayesian Modeling: hierarchical GLMs, shrinkage priors, HMC/VI inference

• Scalable Data Pipelines: streaming, batch, distributed processing

I'm looking for opportunities to apply my skills and experience to drive insights and impact in a dynamic team.