Marketing Science Analyst

Accountabilities:: Model monitoring and investigation: Monitor production marketing mix models and investigate alerts involving declining model fit, unusual Return on Ad Spend (ROAS) results and unexpected Customer Acquisition Cost (CAC) fluctuations. Develop investigation hypotheses, analyze relevant datasets, test potential causes and document findings, conclusions and recommendations. Identify and distinguish modeling-related issues from data quality problems, including broken data feeds, incorrect campaign labels, reporting delays and other inconsistencies. Prioritize investigations according to business impact, maintain accurate records and ensure issues are addressed within established timelines. Marketing mix model optimization: Review and adjust production model configurations, including Bayesian priors, seasonality, holidays, promotions, trends and marketing channel classifications. Evaluate modeling parameters such as adstock, Hill saturation and other statistical components when investigating performance anomalies. Compare alternative model configurations against existing production models, assessing statistical fit, channel-level results and overall business plausibility. Validate proposed configuration changes, promote approved adjustments and document rejected recommendations with supporting analysis. Maintain comprehensive records of model configurations, modifications, validation results and technical decisions. AI-assisted data analysis: Use AI-powered coding agents, including Claude Code, to accelerate investigations, analyze model behavior and identify data discrepancies. Review and interpret Python and SQL code to understand modeling pipelines, investigate analytical workflows and troubleshoot technical issues. Critically validate AI-generated analyses, calculations and recommendations against underlying datasets and statistical evidence. Integrate AI-assisted workflows into daily analytical activities while maintaining accuracy, consistency and sound analytical judgment. Cross-functional collaboration and documentation: Work closely with Data Science and Customer Success teams to communicate investigation results and coordinate issue resolution. Translate complex statistical and modeling concepts into clear, actionable recommendations for technical and non-technical stakeholders. Track investigation queues, technical issues and resolution progress using Jira. Maintain concise investigation reports, configuration change logs and technical documentation to facilitate collaboration, knowledge sharing and effective handovers. Performance and continuous improvement: Work toward defined operational objectives, including reviewing new model alerts and documenting initial hypotheses within one business day. Aim to identify root causes and provide recommendations within three business days of an investigation. Maintain a high level of investigation accuracy, targeting at least 90% correct classification of data-related versus model-related issues. Ensure every approved or rejected configuration change is supported by documented comparisons of model fit and business plausibility. Progress toward independently managing the complete model alert investigation process within the first 60 days. Requirements: Minimum of 3 years of professional experience in marketing analytics, data analysis, applied data science or a related analytical discipline. Demonstrated experience investigating data discrepancies, performance anomalies and technical issues within analytical systems or data-driven environments. Practical experience reading, interpreting and making minor modifications to Python code, alongside strong working knowledge of SQL. Understanding of statistical modeling concepts, particularly Bayesian regression, marketing attribution, seasonality, adstock and saturation. A degree in Statistics, Economics, Mathematics, Engineering or another quantitative discipline, or equivalent practical professional experience. Strong analytical and problem-solving skills, with the ability to investigate complex modeling pipelines and identify potential sources of errors. Sound analytical judgment when distinguishing data quality issues from modeling problems and evaluating the reliability of analytical outputs. Experience using AI coding agents to support technical investigations, accelerate data analysis and validate analytical findings. Ability to assess model configurations using both statistical performance indicators and business plausibility. Strong understanding of marketing performance metrics, including channel-level ROAS and marketing effectiveness. Nice to have: Previous experience in marketing analytics or media buying, particularly when evaluating advertising channel performance and return on investment. Experience with Marketing Mix Modeling (MMM) or causal inference in an agency, technology vendor or in-house environment. Familiarity with Bayesian marketing mix modeling methodologies and tools such as PyMC. Experience analyzing e-commerce and advertising datasets from platforms such as Shopify, Amazon Ads, TikTok Shop, Meta and Google Ads. Practical experience with Claude Code or other AI-powered coding and analytical tools. Familiarity with AWS S3, AWS Athena, BigQuery, Hex and GitHub. Experience using Jira for investigation tracking, project coordination and technical documentation. Strong organizational skills, attention to detail and the ability to prioritize multiple investigations according to urgency and business impact. Ability to work independently, manage deadlines and communicate technical findings clearly within a remote, cross-functional team. Availability to work during US business hours, aligned with the client's time zone, from approximately 9:00 AM to 5:00 PM EST. Benefits: Competitive compensation: Monthly salary ranging from USD $2,000 to $2,400, depending on experience and geographical location. Fully remote work: Work from home with the flexibility and convenience of a distributed working environment. International collaboration: Work alongside experienced professionals in Data Science, Marketing Analytics and Customer Success. Advanced technology exposure: Gain practical experience with AI coding agents, Bayesian modeling, Python, SQL and modern analytical platforms. Professional development: Build specialized expertise in marketing mix modeling, causal inference and marketing performance measurement. Meaningful business impact: Work directly with production models supporting hundreds of brands across leading e-commerce and advertising platforms. Technical growth: Develop hands-on experience investigating complex analytical challenges and improving the reliability of customer-facing insights. AI-driven environment: Contribute to innovative workflows that combine human analytical expertise with AI-powered tools to improve efficiency and accuracy.