Marketing Data Analyst (Python & SQL)

This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client’s time zone.

Our client is a marketing analytics technology company that runs marketing mix models (MMM) for hundreds of brands across Shopify, Amazon, TikTok Shop, and retail channels. Its platform helps brands evaluate marketing performance through automated modeling, data analysis, and channel-level insights.

The Marketing Data Analyst (Python & SQL) will investigate production model alerts, troubleshoot data and modeling issues, and optimize marketing mix model configurations to maintain accurate and reliable results.

This role sits within the Data Science team and focuses on investigating anomalies, validating model performance, and using AI coding agents to accelerate analysis. The ideal candidate has strong analytical judgment, familiarity with statistical modeling concepts, and the ability to interpret Python and SQL while translating complex findings into actionable recommendations.

• Alert Triage: Every new model alert is reviewed, with an initial hypothesis documented within 1 business day.
• Alert Resolution: Root causes and recommendations are documented within 3 business days.
• Investigation Accuracy: At least 90% of investigations are correctly classified as data-related or model-related issues.
• Configuration Documentation: Every approved or rejected model configuration change is documented with model fit and plausibility comparisons.
• Independence: Independently manage the complete model alert investigation process by day 60.
• Cross-Functional Trust: Data Science and Customer Success teams can rely on accurate, timely investigation findings and recommendations.

Experience

• 3+ years of professional experience in marketing analytics, data analysis, applied data science, or a related analytical role.
• Experience investigating data discrepancies, performance anomalies, or issues within analytical systems.
• Experience reviewing Python and SQL code to understand data pipelines and analytical workflows.
• Experience working with statistical modeling concepts, including Bayesian regression, seasonality, attribution, adstock, or saturation.
• A degree in statistics, economics, mathematics, engineering, or a related quantitative field, or equivalent hands-on experience.
• Experience in marketing analytics or media buying, including evaluating channel-level ROAS, is preferred.
• Experience with marketing mix modeling (MMM) or causal inference in a vendor, agency, or in-house environment is preferred.
• Experience analyzing e-commerce and advertising data from Shopify, Amazon Ads, TikTok Shop, Meta, or Google Ads is a plus.

Skills

• Strong ability to read, interpret, and lightly modify Python code and work with SQL.
• Understanding of Bayesian regression, marketing attribution, seasonality, adstock, and saturation concepts.
• Ability to investigate complex modeling pipelines and identify potential sources of errors.
• Strong analytical judgment when distinguishing data quality issues from modeling problems.
• Proficiency in directing AI coding agents through investigations and validating their findings.
• Ability to evaluate model configurations based on statistical fit and business plausibility.
• Familiarity with PyMC and Bayesian marketing mix modeling concepts.
• Ability to document hypotheses, investigation findings, and recommendations concisely.
• Strong attention to detail and ability to prioritize investigations based on business impact.
• Familiarity with Jira and AWS S3 is preferred.
• Familiarity with tools such as AWS Athena, BigQuery, Hex, Claude Code, and GitHub is a plus.

Fully Remote (Work from Home) | 9:00 AM – 5:00 PM EST

• Investigate production model alerts, including declining model fit, unusual Return on Ad Spend (ROAS), and Customer Acquisition Cost (CAC) spikes.
• Develop hypotheses, analyze relevant data, test potential causes, and document findings and recommendations.
• Distinguish modeling issues from data problems such as broken feeds, incorrect campaign labels, or reporting delays.
• Prioritize investigations and maintain accurate records of findings and resolutions.

• Review and adjust production model configurations, including priors, seasonality, holidays, promotions, trends, and channel labeling.
• Evaluate adstock, Hill saturation, and other modeling parameters when investigating performance issues.
• Compare candidate configurations against live models based on model fit and channel plausibility.
• Promote validated configurations or document why proposed changes were rejected.
• Maintain accurate records of model changes and configuration decisions.

• Use AI coding agents, including Claude Code, to investigate model behavior and data discrepancies.
• Review Python and SQL code to understand modeling pipelines and troubleshoot issues.
• Validate AI-generated analyses, calculations, and recommendations against underlying data.
• Use AI-assisted workflows to accelerate investigations while maintaining analytical accuracy.

• Collaborate with Data Science and Customer Success teams to communicate investigation findings.
• Translate complex modeling issues into practical recommendations for marketing stakeholders.
• Track investigation queues and technical issues in Jira.
• Maintain concise investigation notes, model change logs, and documentation to support follow-up and handoffs.

The Marketing Data Analyst (Python & SQL) will have the opportunity to work directly with production marketing mix models used by hundreds of brands, investigating complex analytical issues and improving model reliability.

This role offers hands-on exposure to Bayesian modeling, AI-assisted analysis, and marketing performance measurement while contributing directly to the accuracy of customer-facing insights.

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