Forward - Senior Data Analyst, Advanced Analytics & Data Science

Conduct advanced data analysis, exploratory data analysis, statistical modeling, and hypothesis testing to identify trends, performance drivers, and actionable business insights.

Analyze Retail Media Network (RMN) campaign performance, attribution, return on ad spend (ROAS), and incrementality to evaluate advertising effectiveness and identify optimization opportunities.

Develop and implement predictive models, forecasting methodologies, customer and campaign segmentation, and machine learning solutions to address business challenges.

Design, build, and maintain interactive dashboards, analytical data models, and scalable reporting solutions using Tableau, Looker, or comparable business intelligence platforms.

Leverage SQL, Google BigQuery, and Python to extract, transform, analyze, and validate large datasets, ensuring analytical outputs are accurate, reliable, and scalable.

Apply statistical and data science methodologies to measure campaign effectiveness, evaluate performance drivers, and support advertising measurement and optimization strategies.

Automate recurring reports, analytical workflows, data validation processes, and other manual activities to improve operational efficiency and scalability.

Partner with client stakeholders and cross-functional teams to understand business requirements, define analytical approaches, and deliver data-driven recommendations.

Translate complex analytical findings into clear insights, visualizations, and presentations for both technical and non-technical audiences.

Manage analytical projects from discovery through delivery, proactively coordinating timelines, dependencies, risks, and deliverables.

Identify opportunities to improve analytical methodologies, reporting processes, experimentation, and predictive capabilities through continuous innovation.

Maintain comprehensive documentation of analytical methodologies, data definitions, business logic, and reporting processes to support consistency, transparency, and governance.

Provide guidance and mentorship to junior analysts, promoting best practices in data analysis, statistical modeling, reporting, and stakeholder communication.

4–7 years of professional experience in data analytics, business intelligence, advanced analytics, data science, or a related quantitative field, with demonstrated experience applying analytical techniques to business problems.

Strong proficiency in SQL and Google BigQuery, including querying large datasets, performing data transformations, developing analytical models, and optimizing query performance.

Hands-on experience with Python and analytical libraries such as Pandas, NumPy, SciPy, Scikit-learn, Statsmodels, or equivalent tools.

Demonstrated experience with statistical analysis, exploratory data analysis, hypothesis testing, regression, forecasting, segmentation, and other advanced analytical techniques.

Strong experience developing dashboards, visualizations, and reporting solutions using Tableau, Looker, or comparable business intelligence platforms.

Experience applying analytics to business performance measurement, campaign effectiveness, marketing analytics, advertising measurement, or similar domains.

Familiarity with advertising measurement concepts, including attribution, ROAS, incrementality, campaign optimization, and performance measurement, is highly desirable.

Experience automating analytical workflows, recurring reporting, data validation, and manual processes using SQL, Python, or scripting tools.

Strong understanding of data quality, metric definitions, data validation, analytical documentation, and data governance practices.

Ability to translate complex business questions into structured analytical approaches and communicate findings through clear, actionable business recommendations.

Demonstrated ownership of analytical projects, including the ability to independently manage multiple priorities, timelines, and deliverables in a fast-paced, cross-functional environment.

Strong communication, collaboration, and stakeholder management skills, with the ability to work effectively in a client-facing environment.

Experience working with Retail Media Networks (RMNs), digital advertising, e-commerce analytics, or marketing measurement.

Experience evaluating campaign incrementality and attribution methodologies to distinguish observed performance from true incremental impact.

Familiarity with experimentation frameworks, A/B testing, causal inference, and measurement methodologies for assessing campaign effectiveness.

Experience developing and deploying predictive models or machine learning solutions to support business forecasting, segmentation, and optimization.

Experience working with large-scale cloud data platforms and modern analytical data environments.