Data Science Strategist

Finn Partners•,

10d•$85,000 - $110,000•Remote

About The Position

The Data Science Strategist sits on the Data Science team within Integrated Marketing and combines hands-on technical work with data strategy. The role focuses on building dashboards, connecting data sources, running measurement models, and translating client and business questions into practical measurement solutions. The strategist partners with account, media, social, SEO, and client teams to plan measurement and communicate clear, actionable findings. The position builds and maintains dashboards, reporting solutions, data pipelines, calculators, audit and QA scripts, templates, and lightweight internal applications. It uses NinjaCat and other BI and data-connector tools to bring cross-channel marketing data together, develops data models and QA processes, and pilots AI-powered features in the BI and marketing technology stack with appropriate validation and responsible use. The role also develops and interprets multi-touch attribution analyses and media mix models using varied tools and methods, then recommends budget allocation, optimization, incrementality, and testing approaches. It keeps current with marketing technology, privacy changes, AI, and measurement methods. The role is remote-first and may be performed anywhere in the United States, with optional in-office days in several listed cities.

Requirements

4–7+ years of experience in marketing analytics, data science, or business intelligence; agency or consultancy experience is ideal.
Working understanding of paid search, paid social, programmatic, SEO, email, and web analytics.
Proven dashboard-building and BI experience, such as NinjaCat, Looker Studio, Power BI, or Tableau.
Experience building data tools and automations beyond dashboards, such as scripts, pipelines, templates, or small applications.
Comfort using AI tools for coding, analysis, automation, and documentation, with the judgment to check and validate their outputs.
Hands-on expertise with Google Analytics 4 and Google Tag Manager.
Experience building or applying multi-touch attribution and media mix modeling using more than one toolset or methodology.
Strategic mindset, including listening, asking questions, and connecting data work to business outcomes.
Excellent communication skills and ability to explain technical concepts to non-technical audiences.
Ability to manage multiple client projects and deadlines concurrently.

Nice To Haves

Experience with NinjaCat.
SQL proficiency and experience with Python or R for analysis and modeling.
Experience with cloud data warehouses such as BigQuery or Snowflake.
Familiarity with privacy and consent frameworks, including consent mode, server-side tagging, and first-party data strategies.
Experience in regulated industries such as healthcare.

Responsibilities

Partner with account, media, social, SEO, and client teams to understand goals and translate business questions into measurement plans and KPIs.
Diagnose data problems, including tracking gaps, disconnected data sources, and conflicting numbers, and recommend solutions using available tools.
Design measurement frameworks for integrated campaigns across paid, owned, and earned channels.
Present findings and recommendations to internal stakeholders and clients in clear, actionable language.
Build, automate, and maintain client and internal dashboards and reporting solutions.
Use NinjaCat and other BI and data-connector tools to unify cross-channel marketing data into scalable reporting.
Build data tools beyond dashboards, including automated pipelines, calculators, audit and QA scripts, reporting templates, and lightweight internal apps.
Explore and pilot AI-powered features in the BI and marketing technology stack, and help guide responsible AI use in data work.
Develop data models and QA processes to ensure reporting is accurate, consistent, and trustworthy.
Develop and run multi-touch attribution analyses to assess channel contribution across the customer journey.
Build and interpret media mix models using applicable open-source, platform-native, or custom tools and models.
Recommend budget allocation and optimization strategies based on attribution and media mix modeling, including incrementality and testing approaches where appropriate.
Stay current on marketing technology, privacy changes, AI, and measurement methods, and bring new ideas and tools to the team.