About the Job
Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to...
We operate globally with offices in the US, APAC, and India. Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.
YipitData isn’t a place for coasting—it’s a launchpad for ambitious, impact-driven professionals.
Key Responsibilities
YipitData is becoming the default intelligence platform for anyone evaluating companies and markets. Systematic Data Products & Feeds are central to that ambition. We make differentiated data easy for quantitative and systematic investors - as well as sophisticated fundamental investors with dedicated data teams - to discover, evaluate, trust, integrate, and use.
We are seeking a strategic business leader to turn this growing portfolio into a major, repeatable growth engine. This leader will own strategy and performance across Quant products, consumer transaction data, consumer receipt data and our broader portfolio of data feeds - including ARR, portfolio strategy, investment allocation, packaging and pricing, go-to-market and service motions, and overall business performance.
As the leader of this business, you will set the direction for the portfolio and unite leaders across Product, Quant, Data, Engineering, Revenue, Client Strategy, and Operations behind one business plan. You will determine where we compete, how we win, and where we invest - building the operating model and commercial motion required to deliver durable client value and financial outcomes.
Own ARR growth, retention, profitability, and overall financial performance.
Translate the strategy into a multi-year business plan with clear targets, assumptions, resource requirements, and risks.
Establish the business scorecard and give the Public Investor Leadership Team a clear view of performance, trajectory, and required decisions.
Determine which markets, buyer segments, and systematic workflows YipitData will pursue- and how we will win.
Shape transaction data, B2B data, Quant products, and other feeds into a coherent portfolio rather than a collection of independent offerings.
Identify opportunities to turn existing data into new products, use cases, and revenue streams.
Allocate investment across the portfolio, making explicit decisions about where to scale, maintain, change course, or stop.
Prioritize capabilities and data assets that can be reused across multiple products, workflows, and markets.
Define a clear portfolio proposition that communicates how YipitData’s Systematic Data products fit together and why they are differentiated.
Own packaging, pricing, bundling, entitlements, and other core business-model decisions.
Partner with Revenue and Client Strategy to build repeatable sales, trial, onboarding, adoption, retention, and service motions for systematic-data buyers.
Build strong relationships with quantitative researchers, data scientists, portfolio managers, and other sophisticated data buyers.
Use client feedback, product usage, pipeline performance, and retention signals to continually refine the commercial strategy.
Create an operating cadence that connects business priorities with product roadmaps, data investments, engineering dependencies, and commercial execution.
Establish clear decision rights and accountability across business, Product, Quant, Data, Engineering, Revenue, and Operations.
Resolve cross-product dependencies and sequencing conflicts so the portfolio operates as one business.
Build the leadership structure, talent, and organizational capabilities required to scale.
Set the business requirements that make systematic products trusted and usable, including point-in-time integrity, history, coverage, stable identities, lineage, joinability, and delivery reliability.
Shape the portfolio’s distribution strategy across feeds, APIs, MCPs, agents, cloud platforms, notebooks, marketplaces, and third-party integrations.
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.
Required Skills & Abilities
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.
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.
