Jobs at YipitData

As a Tech Lead, you will own the technical direction of a B2B analytics platform, lead engineering teams, and ensure production reliability while collaborating with various stakeholders to translate business needs into technical solutions.

Lead data methodology, quality, and accuracy for the Private Investor product. Set data strategy, build QA benchmark sets, author pipeline business logic, apply AI/LLMs for entity resolution and tagging, and scale the data team from an early player-coach to a high-performing organization while supporting customer diligence and trust.

As a Senior Workplace Experience Associate, you will manage facilities, improve processes, oversee vendor relationships, and enhance employee experience in the workplace. This includes planning, compliance, project management, and cross-functional collaboration.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.

Own and QA high-impact retail data products: design and run QA, investigate and resolve complex data quality issues, collaborate with Insights and Engineering, improve automation and product reliability, and communicate methodology and limitations to clients.

Build and own end-to-end, production-grade full‑stack features across backend, frontend, data pipelines, and infrastructure. Collaborate with stakeholders to translate business problems into shipped software, drive cross-team initiatives, and improve tooling and code quality. Use AI coding tools daily to accelerate development and maintain reliability for customer-facing products.

As a Product Manager, you will drive the strategy, roadmap, and execution of data feed products, ensuring they meet both internal and external client needs. Responsibilities include P&L accountability, collaboration with cross-functional teams, overseeing data quality, and enhancing client delivery experiences, while contributing to product vision and market strategy.

Target and break into large enterprise accounts (>$100M software spend) using outbound prospecting (cold calls, emails, LinkedIn) to generate meetings with procurement leaders. Use modern sales tools, collaborate with sales leadership and enablement, refine messaging, and develop toward full end-to-end sales responsibility.

As a Research Analyst, you'll drive fundamental research for subscription-based data products, analyze large datasets, and collaborate with teams to offer insights and develop new products.

Lead end-to-end marketing for YipitData's Investor business, owning demand generation, product go-to-market, customer marketing, campaign execution, and analytics. Partner with Sales, Product, Client Strategy, and Revenue Operations to drive pipeline, retention, and revenue. Build reporting dashboards, optimize performance across channels, and, over time, hire and scale the Investor marketing team.

Own the conversion of raw alternative datasets into scalable, AI-ready data products. Define methodologies, metric definitions, and guardrails for AI-driven insights. Partner with engineering, product, and customers to expand use cases, ensure data quality, validate changes, and manage incidents to deliver reliable, explainable data products at scale.

The Senior Recruiter will lead high-impact recruiting efforts for the GTM organization, focusing on strategic searches across Sales, Marketing, Customer Success, and Strategy. Responsibilities include managing the full recruiting cycle, developing talent pipelines, enhancing candidate experience, and utilizing ATS systems effectively.

The Client Strategy Manager will manage client relationships, develop commercial strategies, and ensure client success through proactive engagement and support. This role includes account management, upselling, and enhancing client engagement with the firm's analytics services.

Lead an end-to-end data domain: define processing and validation, build reusable methodologies and QA/monitoring systems, partner with product and engineering to set technical direction, onboard new datasets, apply AI/automation to reduce manual work, and mentor junior analysts to scale domain capabilities.

Own and maintain data products end-to-end for sector teams, analyze large datasets, refine methodologies, automate workflows using AI and tooling, collaborate with Research, Engineering, and Product, and deliver accurate, client-facing insights and improvements.

Assess and evaluate third-party datasets, define success metrics and analytical frameworks, run quantitative analyses, and recommend product opportunities. Support new product development, collaborate cross-functionally with engineering and partnerships, leverage AI/LLMs and automation, and contribute to tooling, knowledge, and operational efficiency.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Lead the Central Data Platform roadmap: define and prioritize capabilities, translate business needs into technical requirements, coordinate cross-functional execution, measure adoption and data quality, and drive continuous platform improvements to increase reuse and business impact.

Design end-to-end UX for an AI-powered B2B data product. Translate complex AI and data concepts into intuitive, trustworthy interfaces; conduct user research; define workflows; collaborate with Product, Engineering, and Data Science; contribute to design systems and maintain design quality through launches.

Lead operational management of the Product Classification organization: hire, train, and scale classification and manual QA teams; set metrics, SLAs, and operating cadence; partner with Engineering and Product to improve LLM-driven classification; build processes, rules, and analytic checks to ensure high accuracy at scale.