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The Customer Success Manager ensures clients realize measurable outcomes by optimizing their journey and driving product adoption through engagement, training, and collaboration with sales teams.

Drive top-down enterprise sales for AlphaSense’s AI-powered Enterprise Suite. Prospect global enterprises, qualify and manage complex opportunities, conduct technical discovery, design proofs of concept, build business cases and mutual action plans, and guide deals through security, legal, procurement, contracting, and go-live. Partner with account executives, product, engineering, technical, and application teams while engaging developers, architects, IT, InfoSec, legal, procurement, and C-suite buyers.

Sell AlphaSense’s Enterprise Intelligence solution to investment teams and C-suite leaders across hedge funds, private equity, asset management, and investment banking. Responsibilities include prospecting, qualifying opportunities, leading consultative sales processes, coordinating Sales, Product, Engineering, and Technical teams, managing stakeholder communications, developing trial plans, and creating strategies to close enterprise deals.

Own the roadmap and product strategy for private markets, shareholder, and deals financial data. Define requirements, identify content gaps, improve data quality and discoverability, and partner with engineering, design, data platform, and go-to-market teams to launch features and drive adoption. Conduct customer discovery, prioritize backlogs, establish quality assurance strategies, and support diligence, deal-sourcing, and ownership-analysis workflows for financial professionals.

Own the roadmap and product experience for surfacing financial data through search, screening, and agentic workflows. Define requirements, write PRDs, prioritize data discoverability and retrieval improvements, and collaborate with engineering, design, data platform, AI/ML, and go-to-market teams. Lead features from discovery through launch, ensuring search relevance, screening precision, indexing performance, data reliability, and successful adoption for financial professionals and AI agents.

Drafts, reviews, and negotiates complex data, content, licensing, distribution, vendor, and supplier agreements. Advises internal teams on intellectual property, data and content rights, AI and machine learning use cases, privacy, regulatory compliance, and commercial disputes. Partners with Content, Sales, Marketing, Finance, and Product teams; develops policies, playbooks, and project plans; monitors evolving AI and data regulations; and provides broader corporate legal support.

Evaluates and integrates financial datasets and content for an AI-powered market intelligence platform. Leads vendor due diligence, data trials, proofs of concept, quality and coverage assessments, business cases, licensing strategies, competitive research, and content roadmap development. Partners with engineers, content leaders, partnership managers, and vendors to identify high-impact investment research data and AI workflow opportunities.

Own global enterprise consulting accounts and lead complex SaaS sales cycles from strategy through close. Build executive relationships, develop business cases, drive firm-wide platform adoption, generate pipeline through prospecting and ABM campaigns, and partner with account management on renewals, upsells, and cross-sells. The role requires accurate forecasting, quota attainment, strategic selling, and collaboration across sales, marketing, SDR, and account management teams.

Own the technical relationship for enterprise clients adopting AlphaSense’s Enterprise Suite. Lead discovery, proofs of concept, trials, implementations, cloud deployments, API authentication, SSO and SCIM configuration, technical escalations, and account expansion. Partner with Sales, Account Management, Implementation, Engineering, and Product while building scripts and tools to improve client support and implementation workflows.

The Staff Quality Engineer will establish testing strategy, expand scalable end-to-end automation, close coverage gaps, and define quality metrics and release gates. They will test AI-powered features, collaborate with developers on test planning, use AI tools for testing and triage, and coach engineering teams on quality-by-design practices. The role requires influencing multiple teams independently without a dedicated QE team or manager.