Dear Hiring Manager,
A model that scores well in a notebook but never reaches production has helped no one, and I have spent my career closing that gap. At Fenwick Retail I took a churn model that had sat unused for months and built the serving and monitoring around it so it finally drove real retention spend. Shipping models that earn their keep in production is the work I would bring to your team.
Machine learning engineering is where modelling meets software engineering, and I am comfortable on both sides of that line. I build training pipelines that are reproducible rather than heroic, I serve models with the latency and monitoring a production system needs, and I watch for drift and degradation so a model does not quietly rot after launch. I care as much about the feature store and the rollback plan as I do about the architecture.
At Fenwick Retail I productionised a customer churn model that the data science team had built but never deployed. I built the feature pipeline, served it behind a low latency API, and set up drift monitoring with automated retraining. The model reached an AUC of 0.88 in production and the targeted retention campaign it powered reduced quarterly churn by 14 percent among at risk customers.
Your job description mentions building the MLOps foundations as the team grows, which is the work I find most valuable. I would welcome the chance to discuss how I would set up reproducible training and reliable serving from the start. Are you open to a conversation?
Yours sincerely, Grace Aboderin