Data Scientist | Intelligence Systems Builder | AI Engineer

Daniel Richardson

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About

Daniel Richardson

Daniel Richardson

Senior Data Scientist · Intelligence Systems Builder

Pinterest · San Francisco, CA

7+ years of experience building innovative solutions from ambiguous business problems with measurable impact.
4x Pinterest hackathon award winner. Led the development of 'Pinterest Personas' a novel user targeting algorithm that generated over ~$80 million/ year in incremental revenue.

I design infrastructure and AI systems that let teams act on insights, not just data.

I spearheaded Personas, Pinterest's audience targeting algorithm, which clusters behavior across the user graph. What began as a nights-and-weekends exploration grew into an $80M+ product that helps advertisers reach high-intent audiences.

Many tools started the same way: small automation projects that removed repetitive analyst work became core workflows for researchers and GTM teams.

Beyond product, I help build the semantic layer of our insights intelligence infrastructure. This ensures metrics, models, and context are interpretable by both humans and machines. That foundation enables agent-to-agent workflows, letting knowledge move through systems instead of getting stuck in dashboards.

I don't build models for their own sake, I build systems that change how people work and track the numbers to prove it.

Outside work, I chase the same mix of craft and challenge. I've walked the Camino de Santiago, deep water soloed the cliffs of Mallorca, traditional handtool woodworking, and robitics with Arduino and Raspberry Pi projects. Different domains, same principle: pick something hard, learn it deeply, and see it through.

Maker / BuilderExplorer / AdventurerThought LeaderMentor / Connector

Impact at a glance

>$350M

Incremental revenue from Personas targeting algorithm

Pinterest Personas behavioral audience targeting algorithm I developed from a bottoms-up initiative — now a production product generating compounding revenue since 2022.

Company-wide hackathon wins

Across cost savings and advertiser experience tracks at Pinterest.

1000s

Internal stakeholders served

Sellers, researchers, and analysts across Pinterest relying on automation, tooling, and agentic workflows.

20+

End-to-end AI tools & automations built

Internal analytical tools and automation systems led from design through production deployment.

40+

Scaled advertiser research projects led

End-to-end research engagements for Pinterest's largest advertisers (2020–2022), presented directly to client decision-makers.

>$1B

Ad revenue touched by standardized methodology

Owned the standardization and innovation of insights methodologies and shared code used by 50+ global researchers.

11%

Revenue per seller lift

Year-over-year revenue per seller lift associated with automation and intelligence workstreams.

60+

Mentorship reach

Mentees across 5 cohorts in a company-wide data science and analytics mentorship program.

4.0

CMU ML/AI certificate GPA

Graduate-level ML/AI coursework at Carnegie Mellon, focused on depth and rigor.

Featured work

Systems that started as ambiguous problems and ended as production products with real numbers behind them.

Pinterest Personas

Bespoke Persona Generation · Production Targeting Algorithm · Pinterest

Problem

Advertisers could not target the kinds of granular, behavior-based audiences that actually described how people used Pinterest. Keyword and interest targeting left a wide gap — too broad for niche audiences, too shallow in signal. Manual analysis produced interesting decks, but there was a hard ceiling on how many advertisers could benefit and how quickly those insights could refresh.

Insight

Personas fill the gap between keyword and interest targeting. By analyzing more signals over longer lookback windows, they surface stable niche audiences that neither approach can reach. The constraint was not analyst time — it was the representation of people. Cluster behavioral signals into interpretable personas rather than declared attributes, and you unlock entirely new ways to buy media.

Approach

I treated audience definition as an unsupervised learning and product design problem. I built behavioral embedding spaces, experimented with clustering (including HDBScan-style approaches), and personified the resulting clusters so they made sense to sales, product, and advertisers. An Automated Insights Report distills each persona's distinctive characteristics — giving advertisers the language to understand and activate who they're reaching. I then worked with partners to turn those personas into internal tools and, eventually, a full audience product — all without a formal initial mandate.

Impact

What started as a passion project became a production product powering advertiser audience strategy. Personas now support campaigns across global brands and categories and reshaped how we talk about Pinterest's value to the market.
  • $80M+ incremental annual revenue attributable to personas
  • >10K new personas generated annually
  • Adoption across global advertisers and internal sales teams
  • Spawned follow-on work in behavioral audience modeling

GTM Intelligence Platform

Insights innovation and automation · Pinterest

Problem

Sales and analyst teams were stitching together answers from disconnected dashboards, ad hoc SQL, and stale slide decks. The result was slow, inconsistent insights that did not scale with the number of sellers or the complexity of our data.

Insight

If we treated sales intelligence as an orchestration problem rather than a dashboard problem, we could combine structured data marts with semantic vector search and let an agent do the first several hours of work: retrieve, reconcile, and narrate.

Approach

I worked with partners to define a unified data layer, then designed workflows where an agentic system retrieves context, composes text2SQL queries, runs analysis, and generates narrative output for sellers. The goal was not a chat demo; it was a dependable copilot embedded in how teams prepare for conversations and plan territory.

Impact

The platform changed who needed to touch SQL and slideware and when. Sellers and analysts gained faster, more consistent answers, and leadership gained a clearer view of pipeline health and opportunity.
  • +11% year-over-year revenue per seller lift associated with the broader automation workstream
  • Productivity gains across 500+ sellers and analysts
  • Used regularly in executive reviews and planning

LLM Consumer Research Assistant

Advertiser experience hackathon project · Pinterest

Problem

Deep advertiser research meant bouncing between financial filings, news, social sentiment, and internal data. Each engagement could quietly demand hours of synthesis before anyone wrote a single slide or recommendation.

Insight

A retrieval-augmented assistant that can see across these sources, reason about them together, and draft narratives could erase most of the research layer and let humans focus on judgment and strategy.

Approach

I built a RAG system that pulls from financial data, news, and curated social and internal signals, then uses Claude and OpenAI models to synthesize findings into narrative briefs and draft-ready decks. The assistant is designed to be opinionated about structure while transparent about evidence.

Impact

The assistant is on track to automate thousands of research hours per month and to standardize the quality of preparation across teams, not just individuals.
  • 1st place, 2025 Pinterest Hackathon — Advertiser Experience
  • Projected to automate thousands of analyst research hours per month
  • Creates consistent, reusable artifacts for recurring verticals and accounts

Experience

A condensed view of the roles where I learned to design, ship, and scale AI systems.

Jul 2023 – Present

Pinterest

Senior Data Scientist, AI Innovation & Insights Automation

Pinterest

Incubating and scaling AI systems for behavioral audiences, sales intelligence, and agentic analytics automation.

Mar 2022 – Jul 2023

Pinterest

Data Scientist, Insights Innovation

Pinterest

Built automation, clustering, and insights tooling that turned experimental ideas into production workflows and cost savings.

Mar 2020 – Mar 2022

Pinterest

Insights Analyst

Pinterest

Owned a global insights platform and NLP tooling that connected search behavior to advertiser narratives at scale.

May 2019 – Mar 2020

Vega Economics, Statistical and Economic Consulting

Associate

Vega Economics, Statistical and Economic Consulting

Designed NLP and analytical pipelines for expert-witness work on complex financial and healthcare datasets.

Jun 2018 – Apr 2019

Financial Analysis and Management Education (FAME)

Python Developer

Financial Analysis and Management Education (FAME)

Helped build a 25-week quantitative finance curriculum and cloud dashboards that made markets feel tangible for students.

Nov 2017 – Jun 2018

Financial Analysis and Management Education (FAME)

Director of Professional Development

Financial Analysis and Management Education (FAME)

Led student teams through investment research, valuation, and the logistics of running a large student conference.

Aug 2018 – Nov 2018

U.S. House of Representatives (Panetta Institute)

Panetta Institute Intern

U.S. House of Representatives (Panetta Institute)

Researched nuclear, technology, and data policy to support staffers drafting legislation on emerging technologies.

Jun 2018 – Aug 2018

Bedell Frazier Investment Counselling

Data Analyst Intern

Bedell Frazier Investment Counselling

Brought structure to messy financial data through a new MySQL layer and Python-driven integrations.

Full details at LinkedIn

Education

The formal training that underpins how I design and ship AI systems.

Carnegie Mellon University

Graduate Certificate, Machine Learning & Artificial Intelligence

  • Graduate-level ML and AI program focused on statistical learning, representation learning, and production-oriented systems design.
  • Expected completion July 2026.

San Francisco State University

BS Decision Sciences, Honors

  • Applied statistics, operations research, data mining, forecasting, computer simulation, and optimization.
  • Gave me the quantitative toolkit that later anchored my move into data science.

San Francisco State University, Lam Family College of Business

BA Economics, Honors

  • Econometrics, game theory, labor economics, and quantitative finance.
  • Represented the university at the Panetta Institute in Washington, DC and helped run the SF State Student Investment Fund.

Let's talk.

Hard problems · AI systems · Thoughtful collaboration