Data Scientist

This role requires a deep "full-stack" understanding of the digital marketing ecosystem navigating everything from top-of-funnel media buying platforms to bottom-of-funnel web conversions. You will be the primary owner of our attribution logic, stitching together disparate datasets to create a single version of the truth. Beyond technical delivery, you are expected to be a proactive consultant who identifies inefficiencies and scales analytical best practices across the wider Ford of Europe team.

• Digital Marketing Domain Expertise: Deep familiarity with the digital media buying landscape, including Paid Search, Programmatic (DV360), Social (Meta/TikTok), and Organic/SEO.
• Technical Stack: Expert-level SQL and proficiency in Python. Extensive experience with Google Cloud Platform (BigQuery, Dataform) and Git for version control. Ability to build/use AI to improve efficiency.
• AI Automation: Hands-on experience or strong familiarity with applying AI/ML techniques (e.g., Python-based automation, predictive modeling, or utilizing AI tools) to optimize data workflows and improve product efficiency.
• Visualization BI: Proficiency in QlikSense (Set Analysis, QVD generation) and Power BI (DAX, Power Query). Proven experience in leading or supporting a dashboard platform migration is highly desirable.
• Web Analytics: Strong understanding of web tracking technologies, clickstream data, and the mechanics of how impressions and clicks translate into web sessions.
• Attribution Experience: Proven track record of building or managing attribution models in a complex, multi-channel retail environment.
• Proactive Mindset: A demonstrated ability to identify waste, suggest systemic improvements, and lead cross-functional projects from ideation to execution.
• Communication: Ability to bridge the gap between technical data engineering and senior marketing strategy, providing clarity through data and facts.

1.Media Web Data Integration

• Lead the technical integration of media platform data (impressions, clicks, and spend from paid social, search, and direct buys) with internal clickstream and website behavior data.
• Develop and maintain the "stitching" logic required to map the customer journey across various touchpoints, ensuring data continuity between external ad-tech environments and Ford s internal GCP/BigQuery ecosystem.
• Own and optimize data pipelines using Dataform and SQL, ensuring that backend architecture is robust, version-controlled (Git), and ready for high-level visualization.

2. Attribution Modeling Insight

• Design, implement, and sustain advanced Attribution Models (e.g., Multi-Touch, First-Touch, and Last-Touch) to evaluate the effectiveness of marketing spend across all channels (Paid, Organic, and Direct).
• Provide the analytical framework to compare channel performance, helping stakeholders understand the incremental value of different media types.
• Translate complex attribution results into actionable budget-allocation recommendations for Marketing and Sales leadership.

3. Data Transformation AI Efficiency

• End-to-End Data Transformation: Lead the design and execution of robust data transformation (ETL/ELT) processes within GCP/BigQuery using SQL and Dataform, converting raw, multi-channel marketing data into clean, structured tables.
• Cross-Functional Collaboration: Partner with diverse teams including media agencies, regional marketing managers, IT, and visualization developers to translate complex business requirements into scalable data pipelines.
• AI-Driven Efficiency: Proactively identify and implement AI/ML techniques (such as automated data mapping, anomaly detection, predictive modeling, or LLM-assisted coding) to automate manual workflows and improve the overall efficiency of the PMA product.

4. Data Governance Quality

• Establish and enforce rigorous data governance standards across all marketing data sources to ensure high levels of data integrity and "health."
• Act as the primary technical gatekeeper for 3rd party agencies, holding them accountable for the quality and accuracy of the data products they deliver.
• Proactively identify "edge cases" or data gaps in the digital performance reporting (DPR) and lead the remediation efforts to fix them at the source.

5. Proactive Leadership Scaling

• Proactively suggest improvements to current analytical workflows and marketing technologies without waiting for external prompts.
• Act as a mentor and "force multiplier" by documenting code and methodologies, ensuring that specialized knowledge is scaled across the broader analytics team.

• Stay at the forefront of the industry regarding privacy changes (cookieless tracking, consent management) and proactively pivot Ford s strategy to mitigate data loss.