The Judge Group
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with a contract length of "unknown" and a pay rate of "unknown." Key skills include causal inference, experiment design, and hands-on coding in Python or R. Approximately 5 years of relevant experience is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
680
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🗓️ - Date
November 27, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
San Francisco, CA
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🧠 - Skills detailed
#BigQuery #Python #SQL (Structured Query Language) #Datasets #Data Science #A/B Testing #Business Analysis #R
Role description
• Primary responsibility: Proving causal impact through incrementality measurement and geo-experiments.
Key Responsibilities
• Design and interpret experiments (geo-experiments, matched market tests).
• Perform causal inference and statistical analysis (TBR, trim match, causal impact).
• Query large datasets and run models using Python or R.
• Hands-on analytics work; minimal stakeholder management.
• Collaborate mainly with core analysts; limited interaction with PMs/marketers.
Critical Skills (Ranked)
1. Causal inference & experiment design (first principles, not just A/B testing).
1. Statistical understanding.
1. Hands-on coding (Python or R; SQL/BigQuery helpful but secondary).
1. Marketing measurement exposure (nice-to-have).
Experience
• ~5 years hands-on modeling & experimental design (up to 10–12 years acceptable if comfortable with hands-on work).
• Must have actual hands-on experience, not just theoretical or platform-based A/B testing.
Additional Notes
• Dashboarding/pipeline building is secondary.
• No mandatory degree requirement.
• Suggested titles: Marketing Analyst, Data Scientist, Business Analyst (with experimentation), Product Analyst.
• Red flag: Candidates claiming experiment design experience without hands-on work.
• Primary responsibility: Proving causal impact through incrementality measurement and geo-experiments.
Key Responsibilities
• Design and interpret experiments (geo-experiments, matched market tests).
• Perform causal inference and statistical analysis (TBR, trim match, causal impact).
• Query large datasets and run models using Python or R.
• Hands-on analytics work; minimal stakeholder management.
• Collaborate mainly with core analysts; limited interaction with PMs/marketers.
Critical Skills (Ranked)
1. Causal inference & experiment design (first principles, not just A/B testing).
1. Statistical understanding.
1. Hands-on coding (Python or R; SQL/BigQuery helpful but secondary).
1. Marketing measurement exposure (nice-to-have).
Experience
• ~5 years hands-on modeling & experimental design (up to 10–12 years acceptable if comfortable with hands-on work).
• Must have actual hands-on experience, not just theoretical or platform-based A/B testing.
Additional Notes
• Dashboarding/pipeline building is secondary.
• No mandatory degree requirement.
• Suggested titles: Marketing Analyst, Data Scientist, Business Analyst (with experimentation), Product Analyst.
• Red flag: Candidates claiming experiment design experience without hands-on work.
