Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 5


While working at Servicetitan I did the following 
Developed and implemented NLP and ML models, including transformers such as BERT, to classify, cluster, and predict customer interaction metrics, resulting in improved accuracy and efficiency in customer engagement.
 I have trained models from scratch to predict the NPS scores from each client and the CSAT scores from each employee of the client to predict.
 I have also developed a clustering of the customers to identify the customers who are taking too much support resources from my company to reduce the support burden. Based on that model I have created a dashboard which is being used by the Customer Success team to be proactive with the customers. Overall the company started to get less support tickets and continuously saves 10s of thousands of dollars per month.
 I have created a Top Detection dashboard for the Customer Support Leadership team to identify the most common topic per component of the product and analyze its trends by QoQ. The dashboard is being used by team of analysts to identify the product pain points faced by the customers
 Led the development of interactive dashboards in Tableau and other tools to share insights with stakeholders and enable data-driven decision making.
 Designed and created a SQL code parser app with streamlit, which was instrumental in facilitating the team's migration from Redshift to Snowflake, resulting in improved data processing and analysis capabilities.
 Conducted extensive research and testing of hypotheses related to product pain points, resulting in the identification of areas for improvement and cost reduction.
 Spearheaded the use of aggregated tables and optimization techniques, such as stored procedures, airflow dags, and temporary tables, resulting in faster query times and more efficient use of SQL querying across the company.