Data Scientist

Data Scientist We are looking for a Data Scientist to lead a project optimising Google Ads campaigns for long-term, profitable customer acquisition. This role focuses on building predictive models that assess lead quality and expected revenue at the trial level, translating these insights into value-based signals for Google Smart Bidding (Target ROAS). You’ll work closely with marketing, finance, and product teams to ensure signals align with CAC, LTV, and payback period targets, while driving measurable impact on campaign performance. Key Responsibilities: • Develop predictive models to score lead quality and forecast revenue potential. • Translate predictions into stable value-based signals for SEM campaigns, iterating in short test cycles. • Engineer features from CRM, marketing, and product usage data to enhance model accuracy. • Design and run experiments to validate that signal-driven optimizations improve ROAS and customer quality. • Monitor model performance, ensuring stability and mitigating biases. • Collaborate cross-functionally to map predictive outputs to business metrics and communicate results to stakeholders. Required Skills: • Strong experience in machine learning for customer scoring, LTV prediction, or lead quality modeling. • Knowledge of SEM and Google Ads bidding strategies (tCPA, tROAS, Smart Bidding). • Familiarity with SQL, Python/R, and data warehouses (BigQuery, Redshift, Snowflake). • Ability to design experiments and apply causal inference techniques. • Strong business acumen, with understanding of growth funnels, acquisition economics, and profitability metrics. Ideal Candidate: A growth-focused, hands-on Data Scientist with experience in predictive lead scoring or LTV modeling for paid acquisition. Comfortable working in a fast-paced, cross-functional environment, balancing statistical rigor with practical, actionable business insights.