At Exactag, I grew from an intern to a full ML Engineer, building systems that helped clients optimize their advertising spend.
What I Did
I built a recommendation engine designed to optimize advertising spend allocation across channels. The system analyzed historical performance data to identify where budget was being misallocated and suggested optimal distribution strategies.
I owned and maintained ETL pipelines that enabled continuous model training. Using Apache Airflow for orchestration and Hadoop for distributed processing, these pipelines processed large volumes of advertising data and kept our models current.
I developed custom Jupyter Notebook UI plugins that dramatically accelerated the model development workflow. These tools helped data scientists iterate faster on experiments and reduced the friction of common tasks.
As I grew in the role, I took on mentoring responsibilities for junior data scientists, helping them develop technical skills and navigate the organization.
Key Achievements
- •Built recommendation engine that optimized client ad spend allocation
- •Maintained production ETL pipelines using Airflow and Hadoop
- •Created Jupyter plugins that improved model development speed by 2–3×
- •Mentored junior data scientists
- •Earned two promotions: Intern → Jr. Data Scientist → Data Scientist / ML Engineer