Top Tech Jobs & Startup Jobs

Lead end-to-end technical proposal development, including opportunity intake, planning, drafting, compliance, production, and delivery. Partner with business development, engineering, marketing, and company leadership to develop persuasive proposals, identify win themes, maintain proposal templates and libraries, manage CRM workflows, ensure brand consistency, and support business development strategy. The role requires excellent writing, organization, research, communication, and deadline-management skills.

Drive revenue for quantum computing solutions by developing sales strategies, acquiring enterprise customers, managing strategic accounts, forecasting opportunities, leading proposals, and collaborating with technical teams on solution concepts, resourcing, and pricing. The role also supports marketing initiatives and requires expertise in enterprise software and consulting sales, advanced analytics, and relationships with business, technology, and data science executives.

Drive revenue for predictive analytics consulting solutions by acquiring new enterprise customers and growing existing accounts. Responsibilities include market analysis, sales planning, pipeline forecasting, prospect and client relationship development, account management, proposal leadership, pricing collaboration, and partnership with analytics and marketing teams. The role requires technical sales experience with data analytics, machine learning, and AI solutions, particularly within insurance and financial services.

Develop and deploy complex machine learning and analytics models, process large datasets, and deliver solutions through production. The role covers the full data analytics pipeline, including data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. The data scientist will collaborate with clients, software engineers, underwriters, and globally distributed teams while maintaining high-quality, maintainable code and continuing technical development.

Develop and deploy complex machine learning and analytics models for client systems. Manage large datasets, perform data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. Collaborate with clients and distributed data science and systems engineering teams while delivering maintainable, tested, version-controlled, cloud-based solutions within cost and schedule constraints.