Marketing Data Scientist

🎨 About OpenArt OpenArt is an AI Storytelling and Visual Creation Platform used by millions worldwide. We’re building the next generation of creative tools powered by cutting-edge AI, enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we’re shaping that future. 🚀 Why Join OpenArt Ride an inflection point. OpenArt has grown 7–10X in revenue over the past 2 years in one of the hottest categories in tech (AI-native creative tools). You're joining while the trajectory is still steep and every measurement decision compounds. Own the data science practice behind one of OpenArt's biggest growth levers — marketing spend across influencer, paid, and lifecycle. Work across marketing, growth, product, data engineering, and finance — one of the most cross-functional roles in the company. Build from 0 → 1. The attribution model, channel taxonomy, and measurement stack are being laid down right now; you'll help finish the build and then run it. High ownership, low process, fast iteration environment. 🎯 About the Role We're looking for a Marketing Data Scientist to be our first dedicated marketing DS, partnering with our Head of Data to build measurement, experimentation, and insights for OpenArt's marketing org — across performance marketing, influencer/UGC, and lifecycle/email. You don't need to be an expert in every corner of marketing science. What we care about is that you're strong in the fundamentals (SQL, Python, experimentation, at least one measurement specialty), have real product and business intuition , and are hungry to grow into the harder pieces — with a Head of Data who's done all of it available to teach you. You'll inherit and extend work already in flight — our multi-model attribution build, the Marketing Attribution Alignment with the marketing team, and live incrementality tests (Google brand vs non-brand, Director email upsell holdout). You'll also lead areas we haven't been able to cover well yet, most notably real-time influencer/UGC measurement — currently a big gap and a top priority. 🛠 What You’ll Do Marketing measurement & reviews (weekly rhythm) Own the weekly review with the influencer marketing team — bring the data, the read, and the recommendation. Build and maintain real-time dashboards for influencer/UGC traffic spikes, CAC / ROAS / payback across channels, and campaign-level performance. Debug attribution gaps across ad platforms, warehouse, and CRM (Meta Attribution Gap, Impact Radius, Google Ads conversion tracking are all live workstreams). Experimentation & insights Design and run incrementality tests, holdout tests, and A/B tests across marketing programs (email, paid channels, influencer where feasible). Partner on HDYHAU survey design and analysis to correct platform-reported CAC. Do the deep-dive analyses that shape spend decisions — creative reads, cohort/LTV analysis, channel efficiency, campaign post-mortems. Work on the marketing attribution build — channel/subchannel/partner taxonomy, attribution windows, priority chain, promo code handling, multi-model attribution. Contribute to per-creator influencer attribution instrumentation (unique codes / UTMs / landing pages). Grow into ownership of these areas over time as you build depth. Communication Translate insights into crisp, opinionated recommendations that marketing leaders actually use — written and verbal. Set the bar for experimentation rigor and marketing measurement quality at OpenArt. 🧑‍💻 What We’re Looking For Core Requirements 3–6 years of experience in data science, analytics, or marketing science. We'll flex level and title based on candidate strength. Strong SQL and Python — comfortable working with data end-to-end without hand-holding. Meaningful experience supporting a marketing, growth, or lifecycle team — you understand how marketers think and what decisions they need data to make. At least one measurement specialty done well : experimentation/incrementality, attribution, MMM, marketing analytics, or growth analytics. You don't need all of them — you need one that you're actually good at. Strong product/business intuition — you can look at a dashboard and know what's worth chasing. Excellent communication — you can tell a clear data story to a marketer, a PM, or a CFO, and hold your own in a room with strong opinions. High ownership and comfort in ambiguity — you can figure out what needs doing and go do it. What You'll Grow Into We don't expect you to walk in with all of the below — but if you find these interesting and want to build depth here, this role is a great place to do it: Full-stack marketing attribution architecture (taxonomy, windows, multi-model, priority logic) MMM (build, buy, or vendor-partner) Instrumentation and UTM/tracking design at platform level Cross-channel spend strategy and marketing direction Nice to Have Experience measuring influencer/creator/UGC marketing Experience as an early or first data hire supporting a marketing org. Familiarity with our stack: BigQuery, Amplitude, Metabase, Stripe, Google/Meta/TikTok/Bing/Reddit Ads, GTM, Impact, Tolt. Experience in consumer product, marketplaces, creator platforms, or e-commerce. Exposure to AI/ML products or generative AI. Familiarity with privacy-era measurement (Consent Mode, SKAN/ATT, Privacy Sandbox). ⚙ Tech Stack You’ll Work With BigQuery, SQL, Python, Amplitude, Metabase, Stripe, Google Ads, Meta Ads, TikTok Ads, Bing Ads, Reddit Ads, Linkedin Ads, GTM, Impact, Tolt, GCP 💰 Compensation Competitive base salary and bonus program Equity — meaningful ownership in what you build High autonomy, high growth environment 🌍 Work Setup Bay Area preferred (hybrid allowed) Visa sponsorship available We’ll consider remote for exceptional candidates ✉️ How to Apply Send your resume and a short note (links to GitHub/portfolio welcome) to [email protected] . Subject line: Application – Marketing Data Scientist – Optionally include: A marketing measurement, experimentation, or attribution problem you're proud of solving An example of a recommendation you made that changed how a marketing team spent budget Your earliest start date and location/time zone