Aionics is an early-stage startup that uses artificial intelligence and physics-based simulation to design new, customized electrolytes for high performance electrochemical systems. My team and I are designing and driving the implementation of the data, AI and material selection platform that enables internal and external customers to find substances to optimize the performance of batteries.

Luminary Cloud is a computer-aided engineering (CAE) software as a service (SaaS) platform that allows engineers to perform simulations, analysis, and iteration in minutes. Reviewed Luminary’s data back end, made design recommendations, and analyzed and defined AI use-cases. Currently acting as an advisor and consulting on a per need basis.

OwnBackup is a hypergrowth, Pre-IPO, SaaS start-up with more than $150M ARR. It is part of the Sapphire Ventures portfolio and focuses on making data in the cloud secure by providing data backup/recovery, archiving, and sandbox seeding solutions.As OwnBackup's first Data and Analytics leader, I conceptualized and created the Data and Analytics organization from scratch, and was able to recruit and mentor exceptionally talented professionals for roles in Data Engineering, Analytics Engineering and Business Intelligence. We revamped data flows and architecture across systems and developed data products to drastically accelerate business operations and support ARR growth of 60%. By reviewing the Data and Analytics tech stack and data flow (AWS, Snowflake, Fivetran and Tableau) we were able to reduce cost by more than 30%. To ensure alignment between business units and to support the planned S1 filing, we created a Data Dictionary and defined, aligned and implemented 150 core SaaS business metrics across the organization. We adopted agile software development, tripled development speed, and improved quality and adoption of analytic assets.

Aut0 is an Identity and Access Management solution for developers. It was part of the Sapphire Ventures portfolio and has been acquired by Okta in May 2021 for $6.5B. As Auth0's first VP of Data and Analytics, I established a centralized Data and Analytics organization consisting of Machine Learning, Business Analytics, and the Enterprise Data Warehouse teams, and hired and managed an extremely talented international team in US, UK, Spain and Argentina. In the course of my tenure, we improved delivery speed for predictive models and analytics assets by 60% and optimized the alignment with internal stakeholders. We defined, aligned and implemented 120 metrics required to track and measure Customer Success operations, and planned and executed the transition of the data warehouse from Redshift to Snowflake with no impact to business operations. As part of our ongoing work of making the Customer's data more secure, we implemented Token Fraud as well as Bot detection using ML. We also re-negotiated agreements with Looker for real-time analytics for in-product use and saved 70% of cost. As part of the work on MDM, Data Governance, and Data Quality operations, we reviewed data use cases across the organization and defined the permissible usage framework to protect Customer Data and the privacy of our employees and customers. And, finally, we harmonized Auth0 Data and Analytics operations with Okta organization.

At ServiceNow, I was responsible for the Machine Learning team as well as Customer Analytics, Talent Analytics, Customer Support Analytics and Value Realization Analytics. I started by optimizing and aligning the processes utilized by the Machine Learning, as well as Analytics teams to operate at enterprise scale. We reduced delivery times by 50% and dashboard load times by more than 200%. We spearheaded the successful development of shared data platforms, enabling business users to leverage self-serve analytics and implement machine learning models. We revised data access and self-serve analytics processes for Talent Analytics to protect employee privacy while ensuring efficient operations. During my tenure, we increased the adoption of the Customer Dashboard by 35% with more than 70% of the target employees leveraging the dashboards regularly. We also launched alerts and success plays to drive customer adoption and increase customer satisfaction. To measure a customer's adoption of ServiceNow products and to help the Customer Success organization to develop programs, we formulated and implemented the Customer Health Score that ties product feature usage to the value realized by customers. The team was located in the US, UK and India.

As Flexport's first Chief Data Officer, I hired the Data and Analytics leadership team to manage the Data Warehouse, Business Data Analytics, Data Science, and Marketplace Analytics. We doubled the team size, and aligned the group with business units and revised intake and delivery process. Additionally, we developed dashboard trees that cover the operations of the entire organization in Looker.

During my tenure at SurveyMonkey, I merged several groups to form a brand new Data and Analytics organization with the goal of improving the efficiency and effectiveness of business operations, building intelligent product functionality using ML, and delivering insights for the S1 filing and IPO of SVMK. As a first step, we transitioned the team to agile and implemented a business driven prioritization, intake, and acceptance process. This enabled us to triple delivery speed and improve accuracy. We developed a Customer-360 data set to capture and present customer data in Salesforce and serve as foundation for Data Science operations. We migrated the Data Warehouse from private (SQLServer) to public cloud (RedShift), implemented GDPR, and streamlined log data flow for real-time analytics and Machine Learning. As part of our revision of data flows and the data warehouse, we also enabled self-serve analytics for marketing and product teams. Our ML team implemented propensity to buy and churn prediction models as well as multiple in-product AI features for personalization, recommendation and analytics (next question recommendation, answer options, analytics recommendations and similar). As part of the product related ML work, we set up a new ML-Ops function to scale delivery of AI technology for product features (data flow, response time, automated testing, and continuous re-training). We also spearheaded the Data Governance processes and cross-organization alignment of business metrics and drastically improved delivery quality and job satisfaction by embedding Data Analysts within business units.

After having worked on the Customer Success side of Salesforce for 5 years, I moved over to the Sales organization and set up a new team of Data Scientist and Engineers to develop up-sell and cross-sell models to expedite Salesforce’s growth. We improved the efficiency of sales and marketing operations by implementing functionality to prioritize accounts and improve territory carving and were able to accurately predict the top 17% of accounts that generated 86% of worldwide annual contract value. Specifically, we delivered functionality foro Account Prioritization: Which accounts will purchase nexto Next Best Product: Which product should be sold firsto Propensity to Buy: What is the likelihood for an account to acquire a specific producto Sales Relevant Metrics: Which metrics are likely to drive the purchasing behavior of an accounto Comparable Accounts: Which accounts were in a similar state a year ago and can be learned from

As one of Salesforce's first Data Scientists, I started and set up the Customer Intelligence (Customer Data Science) organization at Salesforce. I implemented the Salesforce Early Warning System (EWS) to measure adoption levels of customers and prevent churn. The Early Warning System (EWS) and the associated score were instrumental in reducing churn from the high teens to lower than 5% at the end of my tenure, were part of Marc Benioff's V2MOM (company KPIs), and are still in use today. By working with Salesforce Architects and product teams, we created Adoption Playbooks that enable the Customer Success teams to put the EWS score into action and help customers improve their adoption. My team and I also executed the Customer Success NPS surveys, and devised and deployed ML systems to predict attrition (85% accuracy, 9 months in advance), trigger re-engagement activities and forecast help-desk load (more than 90% accurate).

Responsible for the technology, algorithms and architecture of the recommendation and personalization engine incorporated in Strands’ products. Reviewed and revised the algorithms of the core recommendation engine, devised methods to evaluate recommendation quality and specified an architecture for a recommendation platform that could be leveraged across product lines.

Led a team of data scientists, software engineers, data-warehouse developers as well as usability and subject matter experts. Developed and deployed systems that leverage firm internal and external data sources to support PwC personnel in winning and delivering work. • Project Insight enables PwC personnel to proactively approach potential clients with service offerings. It links and mines large amounts of internal and external data to predict company issues, generates recommendations for potential services, visualizes these results in an easy to understand manner and generates an explanation for its predictions. • Ask a PwC Colleague (a.k.a. The Connection Machine) helps PwC partners and staff solve problems by connecting people with questions to people with answers. The application allows users to enter their question in free text, finds knowledgeable experts with matching profiles, relays the answer and unobtrusively learns and updates user profiles.

In charge of delivering Kaidara’s Case-Based solutions for Knowledge Management, technical self-service and e-commerce to Fortune 500 clients. Developed, customized and integrated web-applications, acquired and modeled know-how from key personnel and information systems, and provided training as well as pre- and post sales support. • Set up the professional services organization of the US Branch of Kaidara, exceeded professional services revenue goals by up to 150%• Developed and delivered close to fifty knowledge management projects as well as pre-sales prototypes to Fortune 500 clients such as Cisco, DaimlerChrysler, General Motors, Rhodia and NSC. Provided training as well as post sales support to clients.

Senior Research Scientist, DaimlerChrysler Research and Technology Center, Adaptive Systems Group (Head: Prof. Pat Langley) in Palo Alto, CA.Designed and developed intelligent systems to provide voice enabled, personalized in-car services (user adaptive recommendations) and to perform predictive, proactive diagnosis and maintenance of vehicles.Research Scientist at the DaimlerChrysler Research and Technology Center, Machine Learning Group (Head: Prof. Gholamreza Nakhaeizadeh) in Ulm, Germany. Primary subject matter expert and researcher on Case-Based Reasoning technology within DaimlerBenz AG. Managed the development of the Case-Based Help-Desk support tool HOMER. Primary contact person for the ESPRIT research project INRECA-II (funded by the European Union). The project produced guidelines for the development of industrial strength knowledge management applications. The result of this work has been published in a book (currently in its 2nd edition).