Data Scientist, Consumer Analytics

Accountabilities:

• Design, develop, validate, and deploy end-to-end analytical and machine learning solutions addressing moderately complex business challenges and customer use cases.
• Partner with product, engineering, and business stakeholders to transform ambiguous business questions into structured problems, testable hypotheses, clear requirements, and measurable success metrics.
• Analyze large and complex datasets to identify consumer trends, behavioral drivers, opportunities, and actionable insights that support business and product decisions.
• Build and maintain scalable data pipelines, analytical features, and machine learning models using modern data and ML technologies, with a focus on reliability, reproducibility, scalability, and production performance.
• Apply experimentation and evaluation methodologies, including A/B testing, backtesting, error analysis, and model evaluation, to measure impact and continuously improve analytical solutions.
• Translate analytical findings and technical concepts into clear recommendations for both technical and non-technical stakeholders.
• Collaborate with cross-functional teams to prioritize analytical initiatives, understand business requirements, and deliver data-driven solutions with measurable impact.
• Contribute to data science standards, documentation, reusable methodologies, and best practices across the broader analytics and data science community.
• Promote responsible and effective use of data and AI by supporting data quality, model governance, evaluation standards, and responsible analytical practices.
• Continuously explore new analytical techniques, tools, and approaches to improve the quality, usability, scalability, and business value of data products.

Requirements:

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or another quantitative discipline, or an equivalent combination of education and experience.
• 5+ years of professional experience applying data science, advanced analytics, machine learning, or a closely related discipline.
• Demonstrated experience developing and implementing end-to-end analytical or machine learning solutions, including data preparation, feature engineering, model development, validation, and operationalization.
• Strong experience working with large datasets and modern data ecosystems, including distributed data processing and/or cloud-based data platforms.
• Proven ability to collaborate with product, engineering, and business stakeholders to understand requirements, prioritize analytical work, and deliver measurable outcomes.
• Strong analytical and problem-solving skills, with the ability to break down ambiguous questions into structured analyses and actionable recommendations.
• Experience with experimentation and model evaluation techniques, such as A/B testing, backtesting, error analysis, or comparable methodologies.
• Ability to communicate complex analytical and technical findings clearly and effectively to both technical and non-technical audiences.
• Strong understanding of data quality, reproducibility, model reliability, and responsible use of data and AI.
• Comfortable working independently while contributing effectively within collaborative, cross-functional teams.
• Curious, pragmatic, and continuously learning, with the ability to adapt to evolving business priorities, analytical challenges, and technology.

Benefits:

• Competitive annual base salary, with location-specific ranges:
  • $125,900–$201,100 in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington State, and Washington, DC.
  • $119,600–$191,000 in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia.
  • Eligibility for equity awards based on factors such as experience, performance, and location.
  • Fully remote work arrangement for employees based in the United States, with limited location exceptions.
  • Compensation designed to comply with applicable state salary thresholds for exempt employees.
  • Opportunity to work on high-impact data science and machine learning initiatives with broad customer and business implications.
  • Collaborative environment bringing together data science, product, engineering, analytics, and business professionals.
  • Opportunities for continuous learning and professional development in modern analytics, machine learning, cloud data platforms, and AI.
  • Exposure to large-scale datasets, production data products, experimentation, and responsible AI practices.
  • Opportunity to contribute to products and solutions that help customers make more informed decisions and navigate important life experiences.