Forward Deployed Marketing Data Scientist

Hightouch; San Francisco (CA); On-site; USD 140,000 - 220,000; Full time

Job summary

A modern AI platform company is seeking a Forward Deployed Data Scientist to work with AI Decisioning customers and internal teams. This role emphasizes both customer-facing and analytical tasks, requiring expertise in data analysis using Python and SQL. The ideal candidate will support AI-driven marketing campaigns by diagnosing model behavior and conducting deep dives into customer data. Strong communication skills are essential to effectively convey insights to both technical and non-technical stakeholders. Competitive compensation ranges from $140,000 to $220,000 annually.

Qualifications

• Experience with Python libraries such as Polars and Pandas in Jupyter notebooks.
• Ability to write and interpret SQL for data analysis.
• High-level understanding of ML modeling, including features and hyperparameters.
• Strong communication skills to convey technical concepts to non-technical stakeholders.
• Customer-oriented mindset with a strong sense of ownership.

Responsibilities

• Own diagnostics, insights, and tuning for AI Decisioning campaigns.
• Work deeply with data in notebooks and customer data warehouses.
• Build lightweight tooling that enables scale in analyses.
• Communicate ML concepts clearly to non-technical stakeholders.

Skills

Deep exploratory data analysis in Python (Polars/Pandas)

SQL for customer warehouse analysis

ML modeling concepts

Excellent communication skills

Customer-first attitude

Job description

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
  • 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.

Qualifications

• 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 or multivariate tests.
• Prior experience in an applied ML, data science, analytics engineering, or forward‐deployed role.
• Experience building lightweight internal tools or scripting solutions.

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.

• Defining the experimental goal and how success should be measured.
• Identifying 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): Discussion about past experiences and future operational preferences to assess fit on company values and operating principles.