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Outreach is a revenue intelligence platform that helps executives, managers, and reps plan, optimize, orchestrate/automate, and guide the execution of sales activities. Being a central place from which sales reps operate gives Outreach a complete view of all activities happening throughout the sales process. My team uses this data and ML to help sales teams be most effective.NLP: Understanding communication between reps and prospects is the foundation of most of our user-facing features. Problems we work on include communication summarization via Generative AI, saving reps time via automated entity extraction from emails, topic/intent/sentiment/question detection in conversations, per-tenant customization, etc.Personalized Recommendation: we strive to provide personalized guidance to reps and managers on actions to take and content to use at any point in the sales process, using LLMs and traditional approaches. Forecasting and Simulation: we help sales leaders with accurate revenue forecasts. We also provide an intuitive framework for exploration and simulation of different scenarios such as resource allocations to help model the future.Optimization: we give our customers the ability to evaluate sales process changes via A/B tests and provide recommendations on process improvements.Data Annotation: we produce high-quality annotated data in-house by analyzing text, voice, and video to support ML model development and evaluation.Product Analytics: we develop standards for collecting, organizing, and using internal data, build dashboards and perform deep-dive analyses, providing insights to product and engineering teams to help make data-driven decisions. ML Engineering Platforms: we develop and maintain tools and infrastructure to support the ML lifecycle: data quality, feature store, model development and experimentation, data annotation platform, model deployment, monitoring, etc.

Data Scientist lead on Analysis and Experimentation Team (http://exp-platform.com). Worked on enabling and scaling experimentation in Windows OS, and improvements to Microsoft's experimentation platform including data pipelines, experiment design and configuration, analysis of experiment results. Previously worked on experimentation in Skype, OneNote, MSN, O365 Support and Bing. Worked with senior executives on defining KPIs for their products and enabling data driven decision making. Helped design, run, and evaluate experiments with multi-million dollar impact across web sites, mobile apps, and backend services. Expert in designing experimentation metrics to accurately evaluate product quality.

Work with various teams at Microsoft to help implement data driven decision making. Specific aspects include collecting the right data, defining clear KPIs and success criteria, learning and measuring impact through online A/B experiments, performing web, usage, and user data mining to extract actionable insights, designing systems and tools to support large scale data analysis.

As part of Bing Data Mining team, worked on variety of data analysis and machine learning projects to mine actionable insights from search log data. Areas of focus included search success metrics and analysis of user behavior on verticals (images, videos, news, etc.)

I founded De-stress.us in response to a lot of stress that many people had to go through during the turbulent economic times. Created during Thanksgiving Holidays, this web site was my way to express gratitude for all that I have in life.

Work with researchers and engineers in Yahoo! Search team to develop next generation crawling and content analysis algorithms. Specific areas include crawl targeting, crawl rate optimization, content quality estimation, real-time content analysis.

Worked on collecting and using explicit and implicit user feedback to improve the quality of Enterprise Search.

Develop a publishing system for creating collections of digital information resources to be used in a distributed digital library environment. Manage a team of developers using RUP methodology.