Lead Data Scientist
New
J
JobgetherGrowth analytics
This is a fully remote opportunity within the United StatesFull-TimeLead
SalaryAnnual salary range of $118,664–$230,619, depending on experience, skills, and location. Location-based compensation ranges, with a stated typical hiring range in California of $139,713–$218,302. Other state-specific ranges are subject to the applicable pay structure.
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Job Details
- Experience
- At least 7 years of professional experience in data science, machine learning, or growth analytics
- Required Skills
- PythonSQLMachine LearningA/B testing
Requirements
- Bachelor’s degree in a STEM discipline required.
- At least 7 years of professional experience in data science, machine learning, or growth analytics.
- Demonstrated record of delivering measurable business impact.
- Expert-level proficiency in SQL and Python.
- Strong background in growth-focused data science, including paid marketing optimization, attribution, funnel analysis, experimentation, and customer segmentation.
- Experience designing and leading complex A/B/n testing frameworks, including multi-armed bandit approaches and causal inference models.
- Hands-on experience developing, deploying, and maintaining production machine learning models.
- Ability to explain complex technical concepts to non-technical stakeholders and communicate recommendations clearly.
- Ability to work through ambiguous problem spaces without established benchmarks or clear ground truths.
- Strong product and business intuition, including partnering with senior stakeholders and influencing strategic decisions.
- A master’s degree or Ph.D. is preferred.
- Familiarity with fine-tuning Generative AI models and large language models for domain-specific applications is an advantage.
- Experience in consumer-focused businesses, fintech, SaaS, or marketplace environments is preferred.
Responsibilities
- Partner with Marketing, Sales, and Customer Experience leaders to identify business challenges, set analytical priorities, and define data-driven growth strategies.
- Develop analyses, predictive models, and analytical frameworks to optimize customer acquisition, lifecycle engagement, and retention.
- Build scalable machine learning solutions for customer targeting, segmentation, uplift modeling, and lifetime value prediction.
- Drive acquisition, retention, and monetization initiatives through experimentation, modeling, and performance measurement.
- Translate analytical findings into actionable recommendations for technical and business teams.
- Collaborate with engineering teams to deploy machine learning models and analytics pipelines into production.
- Identify opportunities to apply current AI and data science solutions to business challenges.
- Establish best practices for experimentation, measurement, and growth analytics.
- Mentor junior data scientists and provide technical guidance.
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