Senior Data Scientist (AI & Media)

Client: Media/Advertising Analytics Organization
Role: Senior Data Scientist (AI & Media)
Salary: $130,000-$145,000
3 days a week onsite 2 days from home

Location: Tysons Corner, VA

We are seeking a Lead Data Scientist to drive the development and deployment of predictive models for a Sales Automation initiative. This role owns the end-to-end data science lifecycle to build machine learning models that predict advertiser probability of conversion to customer, enabling sales teams to prioritize high-value prospects, optimize outreach strategies, and accelerate revenue growth.

Ideal candidates are analytical problem-solvers who excel at translating business objectives into data-driven solutions, can work with complex advertiser datasets, and thrive in a fast-paced, collaborative environment.

• 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 interactive dashboards (e.g., Tableau- or Power BI-style) and generating automated BI reports (Word, PDF, PowerPoint) to 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 models, ensuring scalability, reliability, and alignment with sales and revenue use cases.

• Engineer robust features from multi-source advertiser datasets, including firmographics, engagement history, interaction patterns, and campaign performance; address 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 adoption.

• Design and implement model validation frameworks, including train/test splits, cross-validation, business-aligned metrics (precision, recall, AUC, lift), and rigorous back testing.

• Establish model governance and monitoring practices, including performance tracking, drift detection, retraining pipelines, fairness assessment, and clear documentation of 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 that inform sales strategy and revenue decisions.

• Partner cross-functionally with sales, marketing, and product stakeholders to understand 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.

• Predictive modeling and classification: logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, and ensemble methods.

• Data visualization: creating clear, compelling dashboards and visualizations for technical and non-technical audiences.

• Feature engineering: domain-driven feature creation, feature selection, categorical handling, scaling, and dimensionality reduction.

• Statistical methods: hypothesis testing, confidence intervals, statistical significance testing, and understanding Type I/II errors.

• Imbalanced classification: SMOTE, class weighting, threshold optimization, and appropriate evaluation metrics.

• Model evaluation and validation: cross-validation, ROC/AUC, precision-recall analysis, calibration, and business-aligned KPIs.

• Time series and temporal analysis: handling seasonality, recency bias, and temporal patterns in conversion data.

• Experimentation and causal inference: A/B testing, power analysis, propensity score matching, and incrementality measurement.

• Data wrangling and SQL: extracting, transforming, and aggregating complex advertiser and sales datasets.

• Model deployment and monitoring: model versioning, performance tracking, automated retraining, and production monitoring.

• Communication and storytelling: translating analytical insights into clear, actionable business recommendations.

• 5+ years of applied data science experience with a proven track record of deploying predictive models in production.

• Demonstrated expertise in classification modeling and conversion or propensity prediction (e.g., lead scoring, customer acquisition, churn).

• Hands-on experience with Snowflake, including Snowpark (Python) and/or Snowflake ML / Cortex ML.

• Advanced SQL skills for large-scale analytical datasets.

• Strong proficiency in Python (pandas, numpy, scikit-learn) and SQL.

• Experience with feature engineering on structured business data.

• Comfort navigating ambiguity, forming hypotheses quickly, and iterating toward high-impact solutions.

• Balanced mindset across rigor and speed, with clear understanding of trade-offs.

• Strong collaboration skills and ability to work cross-functionally.

• Commitment to responsible modeling practices, including transparency and fairness.

• Excellent communication skills with the ability to explain complex models to non-technical stakeholders.

• Advanced degree in Data Science, Statistics, Machine Learning, or a related field.

• Prior experience in sales, marketing, or revenue analytics.

• Experience with cloud platforms (AWS, GCP, Azure) and big data tools.

• Familiarity with MLOps practices and production ML pipelines.