Support standard marketing analytics engagements including MMM, test readouts, and performance deep dives
Prepare and QA modeling datasets from multiple sources (ad platforms, adserver logs, web analytics, CRM/sales, research vendors)
Contribute to model development using regression/time-series approaches; help interpret results and document assumptions and limitations
Translate findings into clear deliverables: slides, written summaries, and stakeholder-ready visuals
Help improve Involved’s core offering—linking media to outcomes (leads/sales)—by analyzing drivers such as touchpoints, time-to-convert, device, geo, creative, placement, channel mix, and competitive share-of-voice/spend
Apply the best feasible methodology given the data (e.g., MMM, test design, causal methods, attribution approaches), with guidance from senior team members
Build and maintain reusable analytics components: data pipelines, QA checks, feature generation, reporting automation, and internal tools (notebooks/apps/APIs)
Write clean, well-documented code; use version control and lightweight testing to support reliability and reuse
Collaborate with internal stakeholders (media strategy, client teams, product/engineering) to define requirements and ship improvements
Explore practical applications of AI/LLM tooling to reduce manual work, speed insights, and improve consistency—always with attention to data privacy and quality.

BA/BS (or equivalent experience) in a quantitative field (e.g., analytics, statistics, economics, CS, engineering, applied math)
0–3 years in analytics, data science, marketing analytics, or related work (internships/co-ops count)
Proficiency in Python and SQL for data wrangling, analysis, and basic modeling workflows
Solid foundation in statistics (descriptive statistics, regression fundamentals, experiment concepts, model evaluation)
Ability to explain analysis clearly to non-technical audiences; strong writing and visual communication skills
Comfortable working with imperfect data; resourceful and proactive in diagnosing issues and proposing solutions.
Exposure to marketing measurement methods such as MMM, incrementality testing/matched markets, causal inference, or attribution.
Familiarity with time-series methods and/or modern ML approaches (e.g., tree-based models) and when to use them.
Experience with Google Cloud Platform (e.g., BigQuery) and/or Vertex AI.
Familiarity with web analytics (e.g., GA4), adserver/platform data, or identity/privacy constraints in measurement.
Experience shipping analytics tooling: dashboards, lightweight web apps, scheduled jobs, or internal libraries.

Data Science Analyst

Key skills