Senior Data Analyst (Data Feeds Team) - AI & Product Analytics

About Us

YipitData is the leading market research and analytics firm for the disruptive economy. Our proprietary technology analyzes billions of alternative data points to uncover actionable insights. The world’s top investment funds and Fortune 500 companies depend on our data to drive high-stakes decisions.

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.

What It’s Like to Work Here

• Ownership is real, not aspirational. An analyst here can own a data product end-to-end—from methodology to client delivery—and directly influence products used by many of the world’s largest investment firms.
• Growth is driven by impact, not tenure. Scope and responsibility expand as fast as you can demonstrate you’re ready. We promote based on what you’ve done, not how long you’ve been here.
• AI is a core working tool. We’re actively rebuilding how analytical work gets done—using AI agents, automation, and new tooling to fundamentally change what’s possible. If you’re excited by that, you’ll thrive here.

About the Role

YipitData’s Data Feeds team transforms our proprietary datasets into commercial data products used by many of the world’s largest hedge funds, asset managers, and quantitative investors. Our products help investors answer questions about consumer spending, enterprise software adoption, cloud infrastructure, healthcare, and dozens of other markets—using proprietary datasets that clients integrate directly into their investment workflows.

The Data Feeds team creates products that fit seamlessly into client investment workflows—whether that’s through granular datasets, aggregated metrics, forecasting methodologies, APIs, or entirely new AI-powered product experiences.

As an analyst on the team, you’ll sit at the intersection of analytics, product, and commercialization. You’ll partner closely with Product Managers, Central Data, Data Science, Engineering, Quant Research, and Client Strategy to continuously improve how clients consume and derive value from our data.

We’re hiring across multiple products, including:

• Kepler — consumer transaction datasets powering institutional investment workflows through granular transaction data, aggregated metrics, and forecasting products.
• Summit — B2B spend datasets built from purchase and invoice data, helping investors understand enterprise software, industrials, healthcare, and other sectors.
• Edison — email receipt datasets built from one of the industry’s largest anonymized inbox panels, helping investors track consumer purchases, subscriptions, travel, and digital commerce across thousands of merchants.

Spendhound – proprietary B2B software spend datasets built from ERP, expense, and invoice data, helping investors understand software adoption, customer retention, competitive dynamics, and enterprise technology spending. Cloud – enterprise cloud infrastructure data covering AWS, Azure, Google Cloud, and Oracle Cloud, combining proprietary spend panels with pricing and capacity data to help investors understand cloud growth, AI infrastructure demand, and competitive dynamics.

This role is different from a traditional Data Analyst position. Success isn’t measured by how many analyses you complete. It’s measured by the quality of the products you build, the customer problems you solve, and the leverage you create for clients and the business.

We’re open to hiring this role at multiple levels. While the position is posted as Senior Data Analyst, we encourage applications from candidates with a range of experience who can demonstrate strong analytical judgment, ownership, and growth potential. Final title, scope, and compensation will be calibrated based on experience and demonstrated skills.

As an analyst on the Data Feeds team, you will:

• Own commercial data products – Own products end-to-end, from methodology development and product QA to feature design and ongoing product evolution. Develop deep expertise in how your product works and the investment workflows it supports.
• Improve product quality through systems – Investigate complex data quality issues spanning upstream datasets, product methodologies, and downstream customer outputs. Build scalable monitoring, validation, and QA frameworks that improve quality while reducing manual effort.
• Shape the evolution of the product – Work with Product Managers, Data Science, and Central Data. Identify opportunities for new methodologies, additional datasets, forecasting capabilities, AI-powered features, and entirely new product offerings.
• Evaluate new data and methodologies – Analyze new internal and third-party datasets to determine whether they improve product accuracy, coverage, or customer value. Design experiments, quantify tradeoffs, and help determine what belongs in production.
• Turn customer feedback into better products – Partner with Product Managers, Client Strategy, and directly with sophisticated investors to understand how products are being used. Translate customer questions, trial feedback, and support escalations into durable product improvements.
• Use AI to redesign analytical workflows – Leverage AI agents, automation, and evaluation frameworks to improve QA, documentation, methodology development, and customer-facing analytical experiences.
• Build the next generation of data products – Look beyond maintaining today’s products. Identify opportunities to create new analytical capabilities, improve client workflows, and expand the ways customers interact with our data.

Example Projects

Over your first year, you might:

• Design a new aggregation methodology that significantly improves the accuracy of a commercial transaction product.
• Evaluate a new third-party dataset and determine whether it should become part of a production product.
• Build automated QA monitors that proactively detect regressions before customers notice them.
• Partner with Data Science to evaluate the impact of model improvements on customer-facing metrics.
• Create an AI-powered workflow that dramatically reduces the time required to investigate complex customer-reported data issues.
• Launch a new forecasting capability or AI-powered analytical feature that expands how clients use our products.

You Are Likely To Succeed If

• You have 4+ years of experience in data analytics, with a background in financial services, consulting, data science, product analytics, or another environment where complex data informs high-stakes decisions.
• You have advanced SQL skills and experience using Python or PySpark to build reliable, reusable analytical workflows.
• You enjoy building products—not just analyzing data—and naturally think about how information should be packaged, delivered, and experienced by customers.
• You quickly develop strong mental models for complex datasets and enjoy reasoning through ambiguity to identify root causes and improve methodologies.
• You have led complex cross-functional projects involving Product, Engineering, or analytical stakeholders and are comfortable driving work from idea to execution.
• You know how to balance rigor with pragmatism, making thoughtful tradeoffs between speed, quality, and long-term product value.
• You’re skilled at working with messy, evolving datasets and enjoy creating structure, repeatability, and confidence in complex systems.
• You communicate technical concepts clearly and can explain methodologies, assumptions, and tradeoffs to both technical teammates and business stakeholders.
• You actively use AI tools and are excited about fundamentally changing how analytical products are built—not just making existing workflows more efficient.