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Jordan Sample

Senior Machine Learning Engineer

jordan.sample@example.com · +1 (555) 018-2245 · Remote — United Kingdom · linkedin.com/in/jordansample · github.com/jordansample

Skills

Python · PyTorch · TensorFlow · scikit-learn · SQL · Airflow · Docker · Kubernetes · AWS SageMaker · Feature engineering · Model evaluation · ETL pipelines

Experience

Senior Machine Learning Engineer

2022 — Present

DataCo Solutions

  • Led a cross-functional team building recommendation models that lifted customer retention by 15%.
  • Deployed scalable ML services on AWS SageMaker serving 40M predictions a day.
  • Built a real-time fraud detection pipeline that cut false positives by 40%.
  • Mentored four junior engineers and introduced model review to the release process.

Machine Learning Engineer

2019 — 2022

TechCorp

  • Rebuilt the data ingestion layer, improving throughput 25% and cutting nightly job failures to near zero.
  • Trained and deployed NLP models for support triage, raising first-response relevance by 20%.

Education

  • MSc Computer Science — University of Oxford, 2019
  • BSc Mathematics — University of Manchester, 2017

Modern Professional

ATS-friendly

Accent rules, single column. The safe default for most applications.

Jordan Sample

Senior Machine Learning Engineer

jordan.sample@example.com | +1 (555) 018-2245 | Remote — United Kingdom | linkedin.com/in/jordansample | github.com/jordansample

Professional Experience

DataCo Solutions — Senior Machine Learning Engineer

2022 — Present
  • Led a cross-functional team building recommendation models that lifted customer retention by 15%.
  • Deployed scalable ML services on AWS SageMaker serving 40M predictions a day.
  • Built a real-time fraud detection pipeline that cut false positives by 40%.
  • Mentored four junior engineers and introduced model review to the release process.

TechCorp — Machine Learning Engineer

2019 — 2022
  • Rebuilt the data ingestion layer, improving throughput 25% and cutting nightly job failures to near zero.
  • Trained and deployed NLP models for support triage, raising first-response relevance by 20%.

Education

  • MSc Computer Science — University of Oxford, 2019
  • BSc Mathematics — University of Manchester, 2017

Skills

Python, PyTorch, TensorFlow, scikit-learn, SQL, Airflow, Docker, Kubernetes, AWS SageMaker, Feature engineering, Model evaluation, ETL pipelines

Classic

ATS-friendly

Serif, centred header. Conventional in finance, law and academia.

Jordan Sample

Senior Machine Learning Engineer

jordan.sample@example.com · +1 (555) 018-2245 · Remote — United Kingdom · linkedin.com/in/jordansample · github.com/jordansample

Experience

Senior Machine Learning Engineer, DataCo Solutions2022 — Present

  • Led a cross-functional team building recommendation models that lifted customer retention by 15%.
  • Deployed scalable ML services on AWS SageMaker serving 40M predictions a day.
  • Built a real-time fraud detection pipeline that cut false positives by 40%.
  • Mentored four junior engineers and introduced model review to the release process.

Machine Learning Engineer, TechCorp2019 — 2022

  • Rebuilt the data ingestion layer, improving throughput 25% and cutting nightly job failures to near zero.
  • Trained and deployed NLP models for support triage, raising first-response relevance by 20%.

Education

  • MSc Computer Science — University of Oxford, 2019
  • BSc Mathematics — University of Manchester, 2017

Skills

Python · PyTorch · TensorFlow · scikit-learn · SQL · Airflow · Docker · Kubernetes · AWS SageMaker · Feature engineering · Model evaluation · ETL pipelines

Simply Modern

ATS-friendly

No rules, no colour. Fits the most content per page for long careers.

Jordan Sample

Senior Machine Learning Engineer

Work Experience

Senior Machine Learning Engineer

2022 — Present

DataCo Solutions

  • Led a cross-functional team building recommendation models that lifted customer retention by 15%.
  • Deployed scalable ML services on AWS SageMaker serving 40M predictions a day.
  • Built a real-time fraud detection pipeline that cut false positives by 40%.
  • Mentored four junior engineers and introduced model review to the release process.

Machine Learning Engineer

2019 — 2022

TechCorp

  • Rebuilt the data ingestion layer, improving throughput 25% and cutting nightly job failures to near zero.
  • Trained and deployed NLP models for support triage, raising first-response relevance by 20%.

Two Column

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