Senior Data Scientist

Amit érdemes tudni

• Remote · Ismeretlen
• Távmunka-megjelölés további földrajzi adat nélkül — nem automatikus igen.
• Szint: Senior
• Fizetés: nem közölt

Helyszín és jogosultság

Távmunka-megjelölés további földrajzi adat nélkül — nem automatikus igen.

Remote · Remote

Megbízhatóság: 42%. A végső döntés a munkáltatóé – mindig olvasd el az eredeti hirdetést.

Pozíció

Phantom https://phantom.com is on a mission to connect the world to the freedom of open markets. Tens of millions of people all over the world use Phantom to access global markets that never close, including perpetuals, prediction markets, tokenized assets, stablecoins and memes. Phantom users are able to discover the markets that matter and the cultural moments that shape them, building conviction through real-time data and the verified performance of top traders. With self-custody and access to open networks at its core, Phantom lets them control their financial moves in the same app they use to safely store or spend money worldwide.

Phantom has reached #1 in Google Play's finance category and consistently ranks in the top 50 apps across all categories. Check out our Forbes Fintech 50 https://www.forbes.com/sites/ninabambysheva/2026/02/23/inside-phantoms-battle-plan-to-win-the-crypto-fueled-superapp-war/ and CNBC and Statista's World's Top Fintech Companies 2026 https://www.cnbc.com/worlds-top-fintech-companies-2026/!

We are around 180 people, fully remote, backed by a $150M Series C investment from a16z, Sequoia Capital and Paradigm.

RESPONSIBILITIES

As a Data Scientist at Phantom, you’ll help shape our consumer products through rigorous analysis, experimentation, and reliable data. You’ll work closely with Product, Engineering, Design, and Growth to understand user behavior, identify opportunities, and guide product decisions.

- Analyze user journeys, activation, engagement, retention, and monetization to identify opportunities and recommend product improvements.

- Design and evaluate A/B tests, including hypotheses, success metrics, guardrails, sample sizing, and interpretation. Communicate uncertainty and distinguish observed relationships from causal effects.

- Define and maintain product metrics, and partner with Engineering to improve instrumentation and validate the data behind them.

- Build reusable analytical datasets and dbt models using SQL. Collaborate with Data Engineering on Snowflake, Dagster, and ETL pipelines to improve data quality and support analysis.

- Use Python for statistical analysis, modeling, and automation. Make your work reproducible, documented, and maintainable.

- Incorporate AI coding tools such as Codex, Claude Code, and Cursor into your workflow to accelerate development and analysis. Review and validate generated code, assumptions, and results.

- Independently lead ambiguous problems from initial question th

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