Lead Data Scientist

What you'll work on

• Own the full data science lifecycle for advertiser conversion modeling: problem framing, hypothesis design, feature engineering, model development, validation, deployment, and impact measurement.
• Translate complex analytics and machine learning outputs into consumable business insights by developing Tableau- or Power BI–like interactive dashboards and generating automated BI reports (Word, PDF, PowerPoint) that support executive decision-making and revenue strategy.
• Build and optimize predictive models to estimate advertiser probability of conversion using historical sales data, advertiser behavior signals, engagement metrics, and market trends.
• Leverage managed machine learning and predictive modeling capabilities through cloud platforms (e.g., Snowflake Cortex ML, Snowpark ML) to rapidly prototype, rigorously evaluate, and productionize advertiser conversion probability models, ensuring scalability, reliability, and alignment with sales and revenue business use cases.
• Engineer robust features from multi-source advertiser datasets including firmographics, engagement history, interaction patterns, and campaign performance; handle data quality issues, missing values, and class imbalance.
• Develop classification models (logistic regression, gradient boosting, neural networks, ensemble methods) with strong emphasis on interpretability and business explainability for sales team adoption.
• Design and implement model validation frameworks: train/test splits, cross-validation, business-aligned metrics (precision, recall, AUC, lift), and rigorous backtesting against historical conversion data.
• Establish model governance and monitoring: performance tracking, drift detection, retraining pipelines, fairness assessment, and clear documentation of model assumptions and limitations.
• Create actionable conversion propensity scores and segmentation strategies that enable sales teams to prioritize leads, personalize outreach, and optimize resource allocation.
• Conduct A/B testing and incrementality analysis to measure the business impact of model-driven sales interventions and continuously improve conversion strategies.
• Translate complex model outputs into clear, executive-ready narratives and dashboards; deliver recommendations that drive sales strategy and revenue decisions.
• Partner with sales, marketing, and product teams to understand business requirements, prioritize high-impact opportunities, and design data-informed roadmaps.
• Mentor teammates on best practices in predictive modeling, statistical rigor, and responsible AI; promote a culture of experimentation and measurable impact.

Core Skills and Methods

• Predictive modeling and classification: logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, and ensemble methods.
• Data Visualization: Create clear, compelling data visualizations and dashboards to enable team-led exploratory analysis and effectively communicate to both technical and non-technical stakeholders
• Feature engineering: domain-driven feature creation, feature selection, handling categorical variables, scaling, and dimensionality reduction.
• Statistical methods: hypothesis testing, confidence intervals, statistical significance testing, and understanding of Type I/II errors.
• Imbalanced classification: techniques for handling class imbalance (SMOTE, class weights, threshold optimization) and appropriate evaluation metrics.
• Model evaluation and validation: cross-validation, ROC/AUC analysis, precision-recall curves, calibration, and business-aligned performance metrics.
• Time series and temporal analysis: handling temporal patterns in conversion data, seasonality effects, and recency bias.
• Experimentation and causal inference: A/B testing, power analysis, propensity score matching, and measuring true incremental impact.
• Data wrangling and SQL: extracting, transforming, and aggregating complex advertiser and sales datasets from multiple sources.
• Model deployment and monitoring: model versioning, performance tracking, automated retraining, and production monitoring.
• Communication and storytelling: translating model insights into clear business recommendations and actionable strategies for non-technical stakeholders.

What we're looking for

• 5+ years of applied data science experience with proven track record building and deploying predictive models in production environments.
• Demonstrated expertise in classification modeling and conversion/propensity prediction (e.g., customer acquisition, churn prediction, lead scoring).
• Hands-on experience with Snowflake - Snowpark (Python) and/or Snowflake ML / Cortex ML
• Advanced SQL skills for large-scale analytical datasets
• Experience with feature engineering on structured business data
• Strong proficiency in Python (scikit-learn, pandas, numpy) and SQL for data extraction and feature engineering.
• Comfort navigating ambiguity, quickly forming hypotheses, and iterating toward practical, high-signal solutions.
• Balanced mindset across rigor and speed: you know when to prototype, when to harden, and how to quantify trade-offs.
• Strong collaboration skills and ability to work cross-functionally with sales, marketing, and product teams.
• Commitment to responsible modeling: transparency, fairness, and clearly communicated limitations and risks.
• Excellent communication skills with ability to explain complex models to non-technical stakeholders.

• Advanced degree in Data Science, Statistics, Machine Learning, or related field.
• Prior experience in sales, marketing, or revenue analytics domains.
• Experience with cloud platforms (AWS, GCP, Azure) and big data tools.
• Familiarity with MLOps practices and model deployment pipelines.