Forward Deployed Data Scientist

About Hightouch

Hightouch is the modern AI platform for marketing and growth teams. Our AI agents reimagine marketing workflows, allowing marketers to create content, plan campaigns, and execute strategies with transformational velocity and performance. Hightouch is built at the intersection of advances in LLMs and agentic AI, and the rapid adoption of cloud data warehouses such as Snowflake and Databricks. We partner with industry leaders including Domino’s, Chime, Spotify, Ramp, Whoop, Grammarly, and over 1000 others.

Our team focusses on making a meaningful impact for our customers. We approach challenges with first‑principles thinking, move quickly, and treat each other with compassion and kindness. We look for strong communicators with a growth mindset and a persistent drive for achieving goals.

About the Role

We’re looking for a Forward Deployed Data Scientist to partner closely with our AI Decisioning customers and internal engineering teams, ensuring that AI‐driven marketing campaigns deliver measurable, compounding impact. This role is uniquely cross‐functional : you’ll spend time diagnosing model behavior, tuning ML levers, analyzing incrementality, exploring customer data, and explaining insights to marketers and executives.

Marketing teams come to Hightouch to transform how they operate. AI Decisioning continuously learns preferences and executes 1 : 1 messaging that adapts in real time. Your mission is to make sure that these AI agents perform at their best—and to help customers understand why they are performing the way they are.

Roughly 30% of your time will be customer‐facing and 70% deep analytical and modeling work.

Compensation : $140,000 – $220,000 per year, location independent, remote‐first.

Responsibilities

Own diagnostics, insights, and tuning for AI Decisioning campaigns

• Explain why AI Decisioning is driving lift using counterfactuals, incrementality breakdowns, and cohort analysis.
• Debug performance issues, iterate on reward functions, and ensure the agent’s recommendations align with customer goals.
• Investigate experiment setups (send volumes, reachability, channel constraints) and surface actionable recommendations.

Work deeply with data in notebooks and customer warehouses

Build lightweight tooling that enables scale

Communicate ML concepts clearly to non‐technical stakeholders

Qualifications

Bonus Points

Interview Process

Intro Call (15–30m) : Introductory call with a recruiting team member or hiring manager to discuss fit.

Take‐Home Data Analysis Exercise + Review Session (45m) : Short assignment focused on exploratory data analysis of an example dataset, followed by a live review.

Experiment Design & Analysis Session (90m) : Hands‐on session to design and evaluate an experiment end‐to‐end.

Hiring Manager Interview (30m) : Discussion about past experiences and future operational preferences to assess fit on company values and operating principles.

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