Job description

About the Company & Platform We are a fast-growing, well-funded AI and data analytics startup ($20M raised, currently at 18 employees) building cutting-edge autonomous AI systems for enterprise marketing leaders. Our platform replaces operationally intensive data workflows by deploying agentic systems that handle complex data management, analytics, campaign generation, and measurement entirely on their own. Everything we build sits on top of our proprietary consumer graph—a massive identity and attribute layer covering over 270 million U.S. consumers with more than 2,000 through-time attributes. Our engineering and science team includes world-class operators and researchers from backgrounds like Citadel, Bridgewater, Meta Superintelligence, MIT, and Stanford. We count high-profile enterprises like the NBA, Capital One, the Miami Dolphins, and Ramp among our active clients. What You'll Do This is a generalist role requiring a blend of data engineering fundamentals and advanced statistical thinking. You will own the full path from raw data to production-ready models running at terabyte scale. • Identity Resolution & Data Stitching: Clean, standardize, and stitch disparate, messy third-party data sources into unified, 360-degree consumer profiles. • Probabilistic Modeling: Build and deploy robust estimation models (e.g., predicting income, wealth, affinities, and lookalike/propensity traits) to derive valuable consumer attributes. • Scale Pipelines: Create, refine, and maintain high-throughput feature engineering pipelines that run reliably at terabyte scale. • Autonomous AI Integration: Ensure all model outputs are exceptionally robust and reliable, as they will be consumed autonomously by our downstream AI agents without human intervention. • Client-Facing Solutions: Tackle end-to-end custom enterprise data science work for flagship clients, moving rapidly from messy raw data to deployed production models. Role Requirements • Experience: 2–10 years of applied data science experience. You must have a proven track record of shipping and deploying models directly into production environments (this is not a notebook-only or pure analysis role). • Technical Toolkit: Highly proficient in Python and SQL with the ability to write clean, production-quality code. Strong data engineering fundamentals (data modeling, pipeline construction, data cleaning). • Problem-Solving Mindset: High intellectual horsepower. You are comfortable dealing with ambiguous, unstructured, real-world data across multiple domains in a fast-paced environment. • Background: Open to top-tier industry backgrounds (e.g., tech, ad-tech, or quantitative hedge funds) OR a strong PhD from a top-10 quantitative program looking to transition into a high-ownership startup environment.

Requirements

Must-haves

About the Company & Platform
This is a generalist role requiring a blend of data engineering fundamentals and advanced statistical thinking. You will own the full path from raw data to production-ready models running at terabyte scale.
Identity Resolution & Data Stitching: Clean, standardize, and stitch disparate, messy third-party data sources into unified, 360-degree consumer profiles.
Probabilistic Modeling: Build and deploy robust estimation models (e.g., predicting income, wealth, affinities, and lookalike/propensity traits) to derive valuable consumer attributes.
Scale Pipelines: Create, refine, and maintain high-throughput feature engineering pipelines that run reliably at terabyte scale.
Autonomous AI Integration: Ensure all model outputs are exceptionally robust and reliable, as they will be consumed autonomously by our downstream AI agents without human intervention.
Client-Facing Solutions: Tackle end-to-end custom enterprise data science work for flagship clients, moving rapidly from messy raw data to deployed production models.
Role Requirements
2–10 years of applied data science experience. You must have a proven track record of shipping and deploying models directly into production environments (this is not a notebook-only or pure analysis role).
Technical Toolkit: Highly proficient in Python and SQL with the ability to write clean, production-quality code. Strong data engineering fundamentals (data modeling, pipeline construction, data cleaning).
Problem-Solving Mindset: High intellectual horsepower. You are comfortable dealing with ambiguous, unstructured, real-world data across multiple domains in a fast-paced environment.
Open to top-tier industry backgrounds (e.g., tech, ad-tech, or quantitative hedge funds) OR a strong PhD from a top-10 quantitative program looking to transition into a high-ownership startup environment.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aipythonsql

Platform: manatal
Effort: medium