Position: Machine Learning engineer/MLops engineer
Category: Data Science
English level: upper
Years of experience: 4


Experience
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Machine Learning Engineer
Company A
City A, Country A
October 2021 - Present
- Developed a fraud detection model and deployed it to AWS alongside with a dashboard for business use. The project boosted the amount of found fraud cases by 50%.
- Integrated Feast feature store into an ongoing data science project. Unified feature processing, inference and training pipelines.
- Automated deployment and updates (CI/CD) using GitHub Actions, Docker, AWS(CodeBuild, CodePipeline) increasing update delivery speed.

Machine Learning Engineer
Company B
City B, Country B
October 2021 - June 2021
- Enhanced the quality of models by refactoring feature engineering and model creation steps. Improved model accuracy by 10%.
- Refactored distributed data processing part of a project, optimized memory usage.
- Supported k8s deployments, monitored and optimized resource usage.
- Refactored Slack logs sending system, introduced webhooks mechanism instead of deprecated tokens
- Created several ETL processes using Spark and Kafka.

Data Scientist
Company C
City C, Country C
Dec. 2018 - Oct. 2020
- Took part in IFRS9 modeling and created a new methodology of modeling for various bank products.
- Successfully delivered a pilot project on GCP using BigQuery.
- Created and deployed time-series models. Maintained close communication with customers.
My expectations are:
- Machine Learning Engineer/ Data Scientist/Data Engineer roles
- Looking for part-time jobs, however, might discuss full-time opportunities.