Data Scientist, Consumer Analytics

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist, Consumer Analytics based in the United States.

This is an opportunity for an experienced data scientist to turn complex consumer and business data into insights, models, and scalable solutions that influence meaningful decisions. You’ll work across product, engineering, analytics, and business teams to solve ambiguous problems and translate them into measurable outcomes. The role combines advanced analytics, machine learning, experimentation, and data product development in a collaborative, highly technical environment. You’ll have the opportunity to work with large datasets and modern cloud-based data ecosystems while building production-quality models and pipelines. Your work will help uncover trends, identify opportunities, improve customer experiences, and support smarter business decisions. The position offers significant exposure to cross-functional initiatives and encourages thoughtful experimentation, continuous learning, and responsible use of data and AI. This is a fully remote role available to employees based in the United States, with location-specific compensation ranges.

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.