Head of Data Feeds

YipitData

11d•$225,000 - $250,000•Remote

About The Position

YipitData is seeking a strategic business leader to own the strategy and performance of its growing portfolio of Systematic Data Products & Feeds. This role is central to YipitData's ambition to become the default intelligence platform for evaluating companies and markets. The leader will oversee products including Quant products, consumer transaction data, consumer receipt data, and broader data feeds, managing aspects like ARR, portfolio strategy, investment allocation, packaging, pricing, go-to-market, and overall business performance. The Head of Data Feeds will set the direction for the portfolio, align cross-functional leaders (Product, Quant, Data, Engineering, Revenue, Client Strategy, Operations) behind a unified business plan, and determine competitive strategy, winning approaches, and investment priorities to deliver client value and financial outcomes.

Requirements

Senior leadership experience building or scaling a data, financial-technology, information-services, analytics, or enterprise-software business.
Direct ownership of a P&L or business line, including revenue performance, multi-year planning, resource allocation, and investment tradeoffs.
A record of turning individual products or differentiated data assets into a coherent, repeatable commercial business.
Strong market and portfolio judgment, with the ability to choose among competing buyer segments, growth opportunities, and investments.
Experience developing pricing, packaging, go-to-market, onboarding, and service models for sophisticated enterprise or technical customers.
The ability to build strong client relationships and translate market evidence into clear business and product choices.
Experience leading through a matrixed organization, establishing decision rights, aligning functional leaders, and creating execution discipline.
Sufficient technical fluency to engage credibly on data quality, methodologies, schemas, APIs, infrastructure, and delivery reliability.
A demonstrated ability to build teams, develop leaders, and design organizations that can scale.
Exceptional written, verbal, and executive communication skills, with the ability to turn complexity into clear direction and decisions.

Nice To Haves

Experience serving quantitative investors, systematic funds, data scientists, or other technical investment personas.
Experience in alternative data, market data, financial information services, or investment-research products.
Familiarity with systematic-data requirements such as point-in-time integrity, backtesting, identity mapping, schema stability, lineage, and reproducibility.
Experience commercializing data through feeds, APIs, cloud platforms, MCPs, marketplaces, third-party integrations, or agentic interfaces.
Experience evaluating, sourcing, licensing, or managing third-party data partnerships.
Experience using AI and automation to improve data-product development, quality, evaluation, or client delivery.

Responsibilities

Own business performance, including ARR growth, retention, profitability, and overall financial performance.
Translate strategy into a multi-year business plan with clear targets, assumptions, resource requirements, and risks.
Establish the business scorecard and provide clear performance views to leadership.
Define the portfolio and investment strategy, determining target markets, buyer segments, and systematic workflows.
Shape various data products into a coherent portfolio.
Identify opportunities for new products, use cases, and revenue streams from existing data.
Allocate investment across the portfolio, making decisions on scaling, maintaining, changing course, or stopping initiatives.
Prioritize reusable capabilities and data assets across multiple products and markets.
Build the commercial engine by defining a clear portfolio proposition and owning packaging, pricing, bundling, and entitlements.
Partner with Revenue and Client Strategy to build repeatable sales, trial, onboarding, adoption, retention, and service motions.
Build strong relationships with quantitative researchers, data scientists, portfolio managers, and other sophisticated data buyers.
Refine commercial strategy based on client feedback, product usage, pipeline performance, and retention signals.
Establish a scalable operating model with a clear operating cadence connecting business priorities with product roadmaps, data investments, engineering dependencies, and commercial execution.
Establish clear decision rights and accountability across business functions.
Resolve cross-product dependencies and sequencing conflicts.
Build the leadership structure, talent, and organizational capabilities required to scale.
Prepare the business for the future of data consumption by setting requirements for trusted and usable systematic products (e.g., point-in-time integrity, history, coverage, stable identities, lineage, joinability, delivery reliability).
Shape the portfolio’s distribution strategy across various channels (feeds, APIs, MCPs, agents, cloud platforms, marketplaces, etc.).
Apply AI and automation to accelerate dataset evaluation, quality assurance, documentation, signal testing, support, and client implementation.
Anticipate changes in systematic and agentic investor workflows and translate them into new business and product opportunities.