Director of Pre-Sales Solution Engineer
New York Metropolitan Area
Director, Pre-Sales Solution Engineer wanted by Electrifai, LLC in Jersey City, NJ. Become a trusted advisor and partner to customers, building relationships up to and including C-level executives; lead customer meetings to uncover business pains and present ElectrifAi’s products as solutions; identify unique requirements and assess solution fit; deliver demonstrations of ElectrifAi’s Machine Learning-enabled software products, with talking points and benefit statements aligned to identified pain points and needs; create custom demo applications that address specific customer requirements and leverage ElectrifAi’s Machine Learning as-a-Service solutions, along with other technologies as required; document solution fit through solution architectures, process diagrams, and other mediums; articulate the scope of any custom development required; develop compelling business cases to highlight the return on investment of ElectrifAi solutions; leverage past experience with ElectrifAi’s products in order to successfully partner with Sales to progress opportunities using clear plans, actively participate in deal reviews, and share ownership of each opportunity with the Sales representative; utilize experience with ElectrifAi’s products to collaborate with Sales, Professional Services, and Product to develop winning RFI/RFP responses, Proposals, and Statements of Work, and ensure a smooth hand-off to Delivery teams after each opportunity is won; cultivate relationships with internal ElectrifAi Subject Matter Experts and Leaders, and involve them in opportunities as appropriate; provide open, constructive feedback to ElectrifAi team members; offer continuous feedback to Product and Engineering organizations, for continuous refinement of our products and solutions; actively look for ways to improve process efficiencies, effectiveness, and outcomes; share your experiences and best practices through writing and presenting to appropriate internal and/or external audiences; and travel up to 75% of the time to meet with clients.
Must have a Bachelor’s degree in Computer Science, Electrical Engineering or a related field plus five (5) years of progressive experience in the job offered or a related occupation. Must have five (5) years of experience as a pre-sales engineer or delivery consultant providing machine learning technology to non-technical users; five (5) years of experience in Automated Data Science, including five years of experience with any one or a combination of Hadoop, Spark, Kafka, NoSQL, and Python; five (5) years of experience understanding client business problems and how to utilize technology to overcome those problems or to meet specific goals; five (5) years of experience developing solution architectures and capturing requirements; five (5) years of experience operating in a matrix environment, managing multiple stakeholders and skill sets; three (3) years of experience with ETL and Data Cleansing/Enrichment technologies, including scripting, data transformations, mapping tables, data model optimization, sheet objects, and security for building data analytics products; three (3) years of experience with text analytics technologies and concepts, including text normalization algorithms, Natural Language Processing algorithms such as Convoluted Neural Networks and Word2Vec, semantic matching, and hierarchical scoring using Deep Learning; two (2) years of experience with spend and procurement analytics technologies, including spend optimization techniques such as maverick spend reduction, tail vendor optimization, and spend consolidation; two (2) years of experience formulating sourcing strategy for clients, including strategies on RFP Design, supplier rationalization, contract negotiations, and supplier evaluation modeling; two (2) years of experience with marketing process analysis, including the formulation of campaign strategy for clients including Campaign Design, customer lists, A/B testing, and offer evaluation modeling; two (2) years of experience with analytical marketing models such as propensity scoring, churn warning, clustering, and response uplift; and travel 75% of the time to meet with clients.