- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
- AWS
- Python
- Machine Learning
- Azure
- Ci/Cd
- Kubernetes
- Flask
- ETL
- Kafka
- Tensorflow
- Pytorch
- Scikit-Learn
- Data Visualisation
- MLOps
- Architected and deployed an AWS-based recommendation engine serving 2 million monthly users, increasing conversion rates by 15%.
- Optimised ETL pipelines with Apache Airflow and Glue, cutting data latency from 24 hours to 90 minutes for analytics teams.
- Led cross-functional workshops to identify ML use cases, resulting in three approved projects with measurable ROI.
- Implemented ML observability tooling that reduced model drift incidents by 40% and improved stakeholder trust.
- Mentored junior data scientists on productionisation best practices, fostering a culture of MLOps accountability.
