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 careers@openart.ai.

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