Competitive Intelligence Research Responsibilities:

• Market Strategy: Influence organization-level product direction through data-driven narratives and an in depth understanding of the market landscape. Demonstrated experience operating at scale, and across ambiguous, environments with working knowledge of econometrics. As a quantitative market-strategist, you will blend practical and applied understanding with technical expertise, including pressure-testing data for quality, reliability, understanding data-biases and being solution driven
• Analytics Leadership: Conduct advanced analyses with 3P datasets, develop statistical models and forecasts, and deliver actionable insights that informs market and business strategy. These include, but are not limited to:
• Data onboarding: Identify, onboard, and rigorously evaluate 3P datasets to determine their signal-to-noise ratio and predictive power
• Data triangulation: Triangulate data from many sources of imperfect information. Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods
• Data transformation: Apply quasi-experimental designs (e.g., synthetic control, diff-in-diff) to isolate the impact of exogenous market shocks and competitor actions on internal performance metrics, using 3rd-party behavioral and economic datasets
• Insight and implications: Apply guidance from such analyses to increase the accuracy of forecasts and better understand market trends
• Technical & Methodological Expertise: Act as a recognized professional in a technical or methodological area (e.g., causal inference, bayesian aggregation), driving the adoption of advanced methods and organization-wide best practices that raise the bar for the entire team
• Data Governance & Quality: Ensure data privacy, security, and compliance with organizational standards. Champion data quality frameworks and documentation practices that enable credible reproducible analyses
• Resourceful, adaptable professional with a bias for action

Minimum Qualifications:

• Bachelors degree and a minimum of 6 years of work experience (minimum of 4 years with a Ph.D.) in business intelligence, product analytics, or economic or strategy consulting in a technology environment with increasing scope and impact
• Demonstrated skill to ethically source, validate, and synthesize high-signal insights from people (e.g., stakeholder interviews, skilled conversations, field research, and relationship-based information gathering) while maintaining high standards for privacy, consent, and integrity
• Proficiency in AI-powered tools: Demonstrate working knowledge of Generative AI technologies (e.g., LLM and AI agents) and experience designing, prompting, and orchestrating AI systems (e.g., prompt engineering) to automate data analyses, synthesize insights, and execute multi-step analytical tasks (e.g., prompting agent to clean datasets, build visualizations)
• Practical working understanding of data-analytics tools, and direct experience managing, analyzing, manipulating and interpreting 1P and external 3P datasets
• Experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R)
• Proven experience with statistical analysis including causal inference (e.g., randomized control trials, quasi-experimentation such as synthetic control, diff-in-diff, meta-analyses), and/or bayesian aggregation (e.g., bayesian pooling, hierarchical modeling)
• Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders
• Demonstrated experience thriving in ambiguous environments and shape new analytics organizations or products

Preferred Qualifications:

• Master's or Ph.D. Degree in a quantitative field such as Quantitative Economics or Political Science, Operations Research, Data Science, Computer Science, Physics, Business, or Mathematics
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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