Forward Deployed Marketing 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 a rare company built on the intersection of two fundamental technological shifts: advances in LLMs and agentic AI, and the creation and rapid adoption of cloud data warehouses like Snowflake and Databricks. Building on these tailwinds, we’ve become a leader in AI marketing and partner with industry leaders like Domino’s, Chime, Spotify, Ramp, Whoop, Grammarly, and over 1000 others. Our team focuses on making a meaningful impact for our customers. We approach challenges with first-principles thinking, move quickly and efficiently, and treat each other with compassion and kindness. We look for team members who are strong communicators, have a growth mindset, and are motivated and persistent in achieving our goals. About the Role We’re looking for a Forward Deployed Marketing Data Scientist to partner closely with our AI Decisioning customers and internal engineering teams to ensure 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. Instead of planning campaigns weeks ahead on a calendar, AI Decisioning continuously learns customer 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. You’ll work alongside ML engineers, product managers, Solutions Consultants, and some of the world’s most recognizable brands to improve campaign performance, debug experiments, and identify opportunities for additional lift. Roughly 30% of your time will be customer-facing and 70% deep analytical and modeling work. No two days are the same, but you can expect to: 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 Pull down historical data to run exploratory analyses using Polars / Pandas in Jupyter notebooks Modify and improve customer feature matrices to unlock deep personalization. Conduct deeper warehouse-level SQL analyses when insights aren’t available in the UI. Build lightweight tooling that enables scale Create templates, notebooks, scripts, and repeatable workflows that improve how we analyze performance across customers. Identify systemic gaps and influence the direction of ML reporting and introspection. Communicate ML concepts clearly to non-technical stakeholders Present model insights and recommendations to marketers, analysts, and executives. Explain how the decision engine handles cold start, message transfer learning, exploration vs. exploitation, and more. Partner closely with Solutions Consultants to identify and drive new opportunities for uplift. What We’re Looking For Strong ability to perform deep exploratory data analysis in Python (Polars / Pandas, Jupyter notebooks). Ability to write and interpret SQL for customer warehouse analysis. High-level understanding of ML modeling concepts (features, hyperparameters, reward functions, training windows). Excellent communication skills; able to explain technical reasoning simply and confidently to marketers. A customer-first attitude with high ownership and urgency when resolving issues. Bonus Points Experience setting up and analyzing marketing experiments such as A/B, multivariate tests. Prior experience in an applied ML, data science, analytics engineering, or forward-deployed role. Experience building lightweight internal tools or scripting solutions. We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. We focus on impact and potential for growth more than years of experience. The salary range for this position is $140,000 - $220,000 USD per year, which is location independent in accordance with our remote-first policy. Interview Process We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for. Our interview process evaluates the most important dimensions of the role: analytical depth, customer communication, first-principles problem solving, and alignment with Hightouch’s values. Intro Call [15–30m] Introductory call with either a member of our recruiting team or the hiring manager to get to know each other and see if the role could be a good mutual fit. Take Home Data Analysis Exercise + Review Session [45m] You’ll complete a short take-home assignment focused on exploratory data analysis of an example dataset. In the live review, you’ll walk through your approach, explain your reasoning, and present your findings as if you were guiding a customer through your analysis. We’re looking for clarity of communication, rigor of thinking, and the ability to turn data into actionable insights. Experiment Design & Analysis Session [90m] A hands-on session where we collaboratively design and evaluate an experiment end-to-end. This includes: Defining the experimental goal and how success should be measured Identifying the most relevant context and features to use in modeling Interpreting example results and diagnosing performance patterns Recommending next steps and communicating tradeoffs Hiring Manager Interview [30m] Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.