As Co-Founder of NOS.Email, I am responsible for driving the company’s vision, strategy, and growth. I oversee all aspects of product development, ensuring our solutions effectively address the needs of businesses in managing chargebacks and combating consumer fraud. My role involves guiding the company’s strategic direction, fostering innovation, and leading our team to execute on our mission to revolutionize the sales landscape.

As a Data Science Engineer at Marin Software, I lead a team of engineers in implementing and enhancing criterion-level simulation andmarketsensitivity forecasting models. These improvements have resulted in increased coverage and higher prediction accuracy for advertisingcampaigns.● Guided a team of engineers to implement and upgrade criterion-level simulation and market sensitivity forecasting models, resulting insignificantly improved prediction coverage and accuracy for various advertising campaigns across multiple publishers, including Google, Meta,MSN/Bing, LinkedIn, and Amazon.● Designed and trained regression forecasting models to predict key campaign metrics such as impressions, clicks, and costs. This led to a 95%prediction coverage for clicks, a substantial increase from the previous 65%.● Proficiently comprehended, extracted, and normalized large-scale advertising campaign datasets using SciPy, ensuring data consistency andquality.● Leveraged unsupervised machine learning techniques to enhance forecasting accuracy, achieving a 26% weightedMean Absolute PercentageError (MAPE) for the forecasting engine using Gradient Boosted Decision Tree Regression techniques.● Implemented SpaCy dictionaries to structure and rank campaign similarities, providing machine learning solutions for language and text analysis.This involved designing, building, and deploying models for millions of unstructured keyword datasets assigned to campaigns.● Developed business metric dashboards on Looker by extracting terabyte-sized advertising datasets from Big Query. This enabled real-timetracking of key performance indicators (KPIs) and facilitated actionable insights to enhance mean time to resolution (MTTR) for research andproduction projects.

● Tableau Reporting Automation: Delivered Tableau reports to stakeholders by automating jobs in Airflow and monitored performance.● NLP Model Development: Built and deployed an NLP model for a virtual assistant using machine learning frameworks with large-scale medicaldatasets to predict symptoms.● Model Performance Analysis: Designed and executed A/B testing on several models to analyze model performance.● Machine Learning for Medical Data: Utilized AWS cloud to build a machine learning classifier model that predicted early stages of Alzheimer'swith 91% accuracy by extracting and analyzing millions of medical datasets, with model evaluation using a confusion matrix.● Effective Communication: Uncovered findings and effectively demonstrated results to both technical and non-technical audiences.

● Product Research and Analysis: Led product research and analysis efforts, fostering synergy between Indian and Israeli technology startups.● Client Consultations: Conducted client consultations and assessed tech stacks, recommending a suite of solutions and upgrades for clients' techstacks using SAAS products to enhance efficiency and accelerate product development.● Cost Optimization: Analyzed client costs for cloud servers and implemented AI-powered solutions to optimize resources, resulting in costreduction for growing startups.

● Data Pipeline Development: Responsible for building data pipelines and achieved an 85% reduction in processing time using Hadoop, Presto,and Hive.● Regression Suite Automation: Created scripts to execute regression suites and developed a data pipeline to retrieve test data from SQL forrespective suites.● Data Validation: Developed unit test cases for periodic data validation on the data source.● Reporting and Stakeholder Presentation: Prepared detailed reports and summarized key findings, presenting them to internal stakeholders