[Hiring] Data Engineer @Forbes Advisor

Role Description

We’re hiring a Data Scientist to help us unlock growth through advanced analytics and machine learning. This role sits at the intersection of marketing performance, product optimization, and decision science. You’ll partner closely with Paid Media, Product, and Engineering to build models, generate insight, and influence how we acquire, retain, and monetize users. From campaign ROI to user segmentation and funnel optimization, your work will directly shape how we grow. This role is ideal for someone who thrives on business impact, communicates clearly, and wants to build re-usable, production-ready insights — not just run one-off analyses.

Qualifications

• Bachelor’s degree in a quantitative field (Mathematics, Statistics, CS, Engineering, etc.).
• 5+ years in data science, growth analytics, or decision science roles.
• Strong SQL and Python skills (Pandas, Scikit-learn, NumPy).
• Hands-on experience with Tableau, Looker, or similar BI tools.
• Familiarity with LTV modelling, retention curves, cohort analysis, and media attribution.
• Experience with GA4, Google Ads, Meta, or other performance marketing platforms.
• Clear communication skills and a track record of turning data into decisions.

Requirements

• Own end-to-end modelling of LTV, user segmentation, retention, and marketing efficiency to inform media optimization and value attribution.
• Collaborate with Paid Media and RevOps to optimize SEM performance, predict high value cohorts, and power strategic bidding and targeting.
• Work closely with Product Insights and General Managers (GMs) to define core metrics, KPIs, and success frameworks for new launches and features.
• Conduct deep-dive analysis of user behaviour, funnel performance, and product engagement to uncover actionable insights.
• Monitor and explain changes in key product metrics, identifying root causes and business impact.
• Work closely with Data Engineering to design and maintain scalable data pipelines that support machine learning workflows, model retraining, and real-time inference.
• Build predictive models for conversion, churn, revenue, and engagement using regression, classification, or time-series approaches.
• Identify opportunities for prescriptive analytics and automation in key product and marketing workflows.
• Support development of reusable ML pipelines for production-scale use cases in product recommendation, lead scoring, and SEM planning.
• Present insights and recommendations to a variety of stakeholders — from ICs to executives — in a clear and compelling manner.
• Translate business needs into data problems, and complex findings into strategic action plans.
• Work cross-functionally with Engineering, Product, BI, and Marketing to deliver and deploy your work.

Benefits

• Experience with BigQuery and Google Cloud Platform (or equivalent).
• Familiarity with affiliate or lead-gen business models.
• Exposure to NLP, LLMs, embeddings, or agent-based analytics.
• Ability to contribute to model deployment workflows (e.g., using Vertex AI, Airflow, or Composer).