Aashish Arora

Worked as a technical lead in a data science project in advertising domain. Managed all deliverable and timeline. Led the development of automated end to end airflow pipelines to evaluate Machine Learning models on weekly basis. Led a team of 4 full time employees and formed strong relationship with stakeholders. Designed pipelines to compare our performance against state of the art LLMs like chatGPT and Gemini on weekly basis. Created interactive looker dashboards for stakeholders to review weekly KPIs. Monitored the insights generated weekly and provided insights and improvements areas for machine learning models. Mapping client’s requirements & providing best products to suit their requirements; generating business from existing accounts and achieving profitability & growth.

Utilized analytical and technical expertise to provide insight and proposals to support business improvement. Conducted business analysis to understand business needs and requirements to translate the conceptual designs. Build predictive models using various machine learning tools to predict the possibility of equipment failure.

Working closely with development teams to ensure accurate integration of machine learning models into firm platforms. Developing the team’s capabilities in data science and machine-learning, and apply them to create new data-driven insights. Developing data models, implementing databases, and writing stored procedures, also analyzing content and quality of databases, and recommending data management procedures. Delivering and implementing the project as per scheduled timeline, extending post-implementation and maintenance support to the technical support team and client. Identifying data requirements/designing data warehouse and business intelligence applications to satisfy client business objectives. Creating the requirements analysis, the platform selection, design of the technical architecture, design of the application and development, testing, and deployment of the proposed solution. Applying dimensionality reduction techniques like feature selection (elimination, variance, and correlation threshold) and feature extraction techniques like PCA. Leading a cross-vertical teams., briefing them about the specifications and meeting individual & group targets.