Jam Paydavousi

Experience

• 

  Comtech Telecommunications Corp.

  Aug 2006 - Dec 2012

  Principal Product Manager

  Led engagement with customers and development team to validate and prioritize market requirements and collaborated with visual design team, engineers, and clients to improve performance and enhance usage — increased usage of mobile apps by 31%, and retention by 37 %.Increased product discoverability in mobile app stores, by highlighting competitive advantages; increased uptake by 6% and usage by 9%. Increased customer-base to over 3 million active subscriptions.Implemented new business models that made the partners’ costs more affordable and predictable - increased customer-base by 65% and usage per customer by 28%, despite free alternatives. Show less

• 

  Stretto

  Apr 2013 - Sept 2014

  Director of Product Management

  Established the product management department, defined strategic and tactical product plans. Led the product team to communicate and collaborate effectively with internal and external groups and define a 2 year product roadmap, business requirements, and improve their tactics with both clients and other groups.

• 

  Mobile Ties

  Oct 2015 - now

  Developer of Marketing SaaS Products, Data Scientist, Marketing Strategist

  Provider of Customer Engagement Solution to retailers and small-medium businesses, including AI-based Target Marketing and Sentiment Analysis using Natural Language Processing (NLP). Technologies include SMS-based mobile marketing platform, digital loyalty rewards solution, reputation management enhancement, and marketing strategy consulting services. Designed to increase businesses' revenue contribution from repeat customers, and bring new customers via targeted engagement on social media. Currently serving retailers including QSR restaurants, auto service centers, and general retailers.https://www.mobileties.com Show less

• 

  Cox Communications

  Oct 2022 - Mar 2023

  Data Scientist

   Implemented machine learning models to discover patterns, predict anomalies among KPIs, to reduce costs, increase customer sentiment, and provide further insight on adoptability of the chat product. Extracted information from Oracle SQL and PowerBI dashboards and utilized Python and respective data tidiness, statistical packages, and machine learning models to: o Evaluated existing anomaly detection mechanism that used traditional Statistical Process Control tools; and provided recommendation to change business rules for several thresholds. o Utilized statistical analysis including ADF and KPSS to separate stationary and non-stationary time-series datasets; then used various prediction models including Isolation Forest and Seasonal Decomposition (STL) to validate anomalies, and S-ARIMA (seasonal ARIMA) to predict anomalies.o Applied unsupervised machine learning model (K-Means) to group and identify days and weeks with similar KPIs and establish dynamic upper and lower thresholds for anomaly detection (per group, then used supervised machine learning (KNN) to make daily predictions for 2023. o Performed linear regression & statistical models to discover correlations between call types and handling duration at Cox & offshore sites. Provided recommendations to enhance support call flows, agent training, and redirecting certain call types to chat product and virtual agent (Oliver).o Used pattern recognition techniques and classification models to discover unusual increase in field truck deployments during peak season May-2022 through September 2022; resulted in enhancements in call flow algorithm, updates to agent performance scorecard metrics. Show less