Data Scientist

Digital Resource is a digital marketing agency specializing in SEO, digital advertising, and social media marketing. We've built an internal platform that connects data from the marketing, advertising, analytics, and business systems we use for clients. It makes that data available to our team and to an AI assistant that helps automate reporting and daily operations.

Position overview

We're looking for a hands-on Data Scientist to help own the data behind our client reporting and internal decisions. You'll write SQL and Python, build dashboards and data models, run analyses that turn data into answers, and help keep our data pipelines reliable. When a data sync fails or a report shows numbers that look wrong, you'll help find out why and fix it.

You'll work under the direction of our Tech Ops Manager and alongside our full-stack developer. This role is a good fit for someone with a solid data foundation who wants to grow into more advanced analytics and AI work.

Target time split: about 50% analytics and reporting, 35% data platform support, 15% working with account teams.

Key responsibilities

Analytics and reporting (about 50%)

• Build and maintain data models for clients, campaigns, leads, billing, and time tracking, with consistent metric definitions.
• Build dashboards and recurring reports for leadership and department managers.
• Run analyses using sound statistical methods: significance testing, regression, trend analysis, and basic forecasting.
• Support client profitability analysis (hours against billing) and client health reporting.
• Help prepare the data models our internal AI assistant uses, and check its answers for accuracy.
• Document metric definitions and keep them current.

Data platform support (about 35%)

• Monitor the scheduled jobs that sync data from third-party systems; diagnose and fix failed or stuck jobs.
• Maintain API integrations with advertising, analytics, CRM, project management, and billing systems, including authentication, rate limits, retries, and error handling.
• Build data quality checks so problems in third-party data are caught before they reach a report.
• Maintain database schemas and transformations, testing changes before they ship.
• Contribute to our TypeScript/Node.js platform following established patterns, with code review from the team.
• Follow the platform's access controls so people and automated processes only see data they're authorized to.
• Document incidents and root causes, and keep technical documentation accurate.

Working with account teams (about 15%)

• Build and maintain client dashboards and reports in our client portal, and make sure the numbers match the source platforms.
• Help audit client website tracking (analytics, tag management, forms, calls) and report where conversions are being lost.
• Explain findings to account managers in plain language.

Requirements

• 2–4 years of professional experience in data science, data engineering, or analytics, writing code that runs in production.
• Strong SQL and Python.
• Solid statistics foundations: experimental design, regression, time series and forecasting, and the basics of causal inference. Academic or course-based knowledge is acceptable.
• Experience building dashboards on top of well-designed data (Looker Studio, Looker, Power BI, or similar).
• Experience working with third-party APIs, including OAuth 2.0 authentication, pagination, and error handling.
• Ability to read and contribute to a TypeScript/Node.js codebase, including pull requests and code review.
• Experience with Git and a pull-request workflow.
• Care and good judgment handling sensitive client and financial data.
• Clear written English, and the ability to explain findings to non-technical colleagues.
• Able to work 8 AM–5 PM Eastern Time, Monday–Friday.

Nice to have

• A cloud data warehouse (BigQuery preferred) and a transformation tool such as dbt.
• Web measurement: GA4, tag management, server-side tagging, conversion APIs, consent mode.
• Marketing mix modeling tools (Robyn, Meridian, PyMC-Marketing) or geo-lift and holdout test design.
• Building or extending MCP servers, or connecting a data warehouse to an AI assistant.
• Advertising and analytics platform APIs (Google Ads, Meta Ads, Google Analytics, TikTok).
• CRM platforms (Salesforce, HubSpot) and billing or ERP data.
• Serverless platforms such as Cloudflare Workers, or automation tools such as n8n.
• Experience at a marketing agency or a SaaS reporting company.
• Project management tools such as Basecamp.

What we offer

• A competitive monthly rate and a long-term, full-time engagement.
• Hands-on work with AI, data, and modern marketing technology.
• Room to grow into advanced analytics and AI work.
• A collaborative, supportive team.