Principal Data Scientist - Advanced Analytics
New
J
JobgetherData science
Based in the United StatesFull-TimePrincipal
Salary$170,000–$200,000
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Job Details
- Experience
- At least eight years of progressive experience in data science, analytics, or machine learning
- Required Skills
- AWSPythonSQLMachine LearningA/B testingGenerative AI
Requirements
- Master’s degree in Statistics, Data Science, Computer Science, Economics, Mathematics, Engineering, or a related quantitative discipline.
- At least eight years of progressive experience in data science, analytics, or machine learning, including hands-on work with large-scale data environments.
- Demonstrated experience contributing as a senior technical expert to experimentation, measurement, and advanced analytics initiatives.
- Expertise in marketing experimentation, including experimental design, A/B and multivariate testing, metric development, lift measurement, and incrementality analysis.
- Strong knowledge of causal inference, hypothesis testing, statistical modeling, and statistical power analysis.
- Hands-on experience developing and deploying market mix, attribution, and uplift models.
- Advanced programming skills in Python, Spark/PySpark, and SQL.
- Experience developing and deploying machine learning models, including clustering, classification, regression, neural networks, anomaly detection, recommendation engines, and predictive modeling.
- Experience with large language models, Generative AI solutions, and agent-based frameworks such as LangChain or LangSmith.
- Familiarity with cloud data platforms such as Databricks or Snowflake, and experience deploying analytical solutions in AWS and Azure.
- Familiarity with Kubernetes, Docker, and Git.
- Experience with data visualization and business intelligence tools; Tableau is preferred.
Responsibilities
- Lead the design, execution, and analysis of marketing and product experiments, including A/B, multivariate, geographic, and holdout testing.
- Define and manage the experimentation roadmap, including hypotheses, prioritization, statistical power analysis, and success metrics.
- Develop methodologies for lift measurement, incrementality testing, causal inference, and marketing effectiveness measurement.
- Establish experimentation standards, governance frameworks, and best practices.
- Design, build, validate, and deploy machine learning and Generative AI solutions using structured and unstructured datasets.
- Develop predictive and prescriptive models, including market mix, attribution, and uplift models.
- Lead model development, coding, testing, validation, and production deployment, and establish technical standards.
- Build and maintain reliable, scalable data pipelines.
- Evaluate emerging technologies, machine learning techniques, large language models, and agent-based frameworks.
- Partner with business and technical teams to identify challenges and develop hypotheses and analytical solutions.
- Communicate findings and recommendations to technical teams, business stakeholders, and executive leadership.
- Mentor data scientists and analytics professionals through coaching, technical guidance, and knowledge sharing.
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