Data Scientist
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Job Description
Data Scientist leveraging advanced analytics and machine learning for growth at Forbes Advisor. Collaborating with teams to optimize marketing performance and product user engagement.
Responsibilities:
- 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.
Requirements:
- 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.
Benefits:
- Day off on the 3rd Friday of every month (one long weekend each month)
- Monthly Wellness Reimbursement Program to promote health well-being
- Monthly Office Commutation Reimbursement Program
- Paid paternity and maternity leaves
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