Startup Data Scientist

Startup Data Scientist

We are Magma, the first all-in-one collaboration platform for digital creativity. Think of us as a blend of Google Docs, Photoshop, Discord, and Roblox, designed for a new era of authentic, real-time creation. As the global appetite for art surges, both the video game and content-production industries struggle with the increasing demand for visuals, shifting to remote work, and managing distributed talent. Magma addresses these challenges by placing community and collaboration at the heart of the creative process, empowering everyone—from individual artists to AAA studios—to produce and manage art more efficiently.

Already trusted by 3 million registered users (with 70,000 using the platform daily), Magma streamlines artistic workflows through advanced design and project management tools. We're venture-backed and led by an experienced founding team dedicated to building a business that unlocks creativity for all. Our approach has even earned us the moniker of a potential "Instagram killer," thanks to our focus on real-time, visible collaboration that merges creation, community, and discovery in ways traditional platforms never could.

About the Team

We are a globally distributed, tight-knit group of Techstars startup veterans, developers, product designers, artists, support advocates, and community managers—all united by a passion for collaborative workflows, digital art, gamification, innovation, and community building. Our advisors bring top-tier industry expertise:

• Dan Sturman, ex-CTO of Roblox
• Andrew Reese, ex-Miro, ex-Canva, first GTM hire to scale Miro commercially.
• Michel Bessner, ex-CEO of Toon Boom Animation

Our team is spread across South-East Asia, Europe, and North America, working asynchronously to shape our platform's direction, new features, and integrations.

What You'll Do

1. Partner with the Product Team

   • Own the Product Analytics Plan: Develop and manage success metrics for features and releases, ensuring product decisions are data-driven.

   • Generate Deep Product Insights: Identify opportunities for new features or product improvements, spot potential issues and opportunities early, and inform execution and strategy.

   • Measure Feature Impact: Collaborate with the CPO and product designers to evaluate the success of each feature or release, maintaining a feedback loop for continuous improvement.

   • Establish Clear Dashboards & Weekly Reporting: Present a weekly "Weather Report" that gives the entire team a concise overview of key metrics, product usage trends, and actionable insights.

2. Deeply Analyze User Retention & Conversion

   • Retention & Funnel Analysis: Investigate user engagement, identify friction points in the funnel, and pinpoint how to reduce churn and boost user activation.

   • Conversion Pipeline Optimization: Track and optimize the entire user journey, from discovery to premium upgrades, ensuring continuous growth in both free and paid segments.

   • Identify & Export PQLs: Leverage usage data to pinpoint product-qualified leads and export them via API to our CRM (Attio) to accelerate the sales pipeline.

3. Develop Predictive Models & Recommendations

   • Algorithm Development: Design and implement recommendation strategies—ranging from heuristics to machine learning models—to personalize content for users.

   • Content & Interaction Classification: Build frameworks to classify user-generated content and interactions on the platform, laying the groundwork for more advanced analytics.

   • UGC Trend Discovery: Identify emerging trends in user-generated content, helping the team understand, leverage, and amplify new creative movements.

   • Data-Driven Growth: Use advanced analytics (e.g., user segmentation, A/B testing) to optimize engagement, retention, and revenue strategies.

4. Design & Scale Data Infrastructure

   • End-to-End Data Pipelines: Plan, build, and maintain scalable ETL processes pulling data from various sources (APIs, databases, scraping).

   • Leverage the Modern Tech Stack: Work with MongoDB, PostgreSQL, Clickhouse, and Metabase. Integrate additional third-party tools with ETL processes.

   • Maintain & Expand Python Data Analysis: Oversee and improve our Python data analysis project that runs on a cron schedule to generate data for BI dashboards.

   • Maintain & Expand C++ Event Analysis: Oversee and improve our C++ interactive growth model project.

   • Contribute to TypeScript Analytics Code: Add and maintain Product Analytics code in our TypeScript projects, ensuring robust instrumentation and data integrity.

   • Enable Team-Wide Data Exploration: Create a culture of self-service analytics, giving every team member the tools and knowledge to explore data and derive insights for their own initiatives.

   • Bonus: Familiarity with Docker to containerize data pipelines and services for efficient deployment.

5. Create Business Dashboards & Reporting

   • Executive & Internal Dashboards: Provide leadership with up-to-date analytics on key metrics, product performance, and business health.

   • Investor-Ready Reporting: Collaborate with finance and investor relations to prepare clean, concise data visualizations demonstrating traction and growth potential.

6. Collaborate Across Teams

   • Reporting Structure: Report directly to our Chief Product Officer (CPO), providing actionable insights that inform product decisions and drive user acquisition, retention, and engagement.

   • Support GTM: Partner with the go-to-market team to deliver commercial insights, fueling client conversion and revenue growth.

   • Support CEO: Collaborate with the CEO to strategically present key metrics to current and prospective investors, propelling overall business expansion.

Who We're Looking For

We won't sugarcoat it: this role is demanding—but incredibly rewarding. If you love turning raw data into impactful insights, thrive on full-stack data responsibilities, and want to own your projects from concept to production, this role is for you. You should feel comfortable writing code daily, watching impact of latest feature releases, building ETL pipelines, diving deep into statistical analysis, and communicating with stakeholders in a fast-paced startup setting. When the opportunity arises, bring your business perspective and propose those $1M+ opportunities that propel product innovation and drive significant growth.

Who We're Not Looking For

• Siloed Specialists: If you want to focus only on one narrow aspect of data (e.g., modeling without any data engineering or vice versa), this may not be the right fit.

• Discomfort with Ambiguity: We move quickly and adapt often. Those looking for rigid structures and minimal change might feel out of place.

• Minimal Collaboration: We need someone who proactively partners with product, engineering, and leadership. If you prefer to work alone and never engage stakeholders, you won't thrive here.

• Pure Academic Researchers: While strong technical expertise is a must, we're looking for practical, results-driven approaches rather than purely theoretical.

Tech Stack

• Databases: MongoDB, PostgreSQL, Clickhouse

• Visualization & BI: Metabase, Posthog, Google Analytics

• Languages:

  • Core analytics: Python/SQL

  • Supporting (nice-to-have or willing to learn): C++ and Typescript

• Cloud Services: We tend to rely on self-hosted software but we do use cloud services when applicable.

• Bonus: Docker for containerizing data science workflows

What You'll Need

• Product Analytics Expertise: Proven ability to work closely with product managers to define success metrics, measure feature impact, and generate actionable insights.

• User Retention & Conversion Analysis: Skilled at diagnosing churn, optimizing funnels, and boosting engagement.

• Solid Data Engineering Foundations: