Senior Data Scientist

As a Senior Data Scientist, you will lead the end-to-end development of intelligent systems that optimize digital campaign performance across channels within IQVIA’s Media OS platform. You will transform complex, multi-source campaign, audience, engagement, conversion, cost-efficiency, and ROI data into scalable predictive scoring, ranking, recommendation, and decisioning solutions that improve platform selection, targeting, budget allocation, engagement, and conversion outcomes.

What You Will Do

Analyze campaign performance across platforms and channels, including impressions, audience engagement, conversions, cost efficiency, ROI, lift, and incrementality. Lead the design, development, validation, deployment, and optimization of predictive scoring, ranking, recommendation, personalization, and machine learning solutions for platform effectiveness, audience targeting, campaign planning, activation, budget allocation, and measurement.

Why It Might Be a Fit

Strong analytical thinking, structured problem-solving, and written and verbal communication skills, with the ability to translate ambiguous business needs into scalable data science solutions.

Requirements

• Master’s or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field
• Strong foundation in machine learning, statistics, probability, data analysis, predictive modeling, model validation, and experimental design
• Advanced proficiency in Python and SQL, with hands-on experience using pandas, NumPy, scikit-learn, and related data science libraries
• Hands-on experience with cloud data platforms, preferably Google Cloud or AWS, and data warehouse technologies such as BigQuery, Snowflake, or comparable platforms
• Hands-on experience designing, implementing, deploying, monitoring, and maintaining machine learning models in production
• Demonstrated proficiency with gradient boosting models for predictive scoring, including XGBoost, LightGBM, CatBoost, or comparable frameworks
• Experience with ranking models or learning-to-rank approaches and appropriate ranking evaluation metrics
• Experience performing feature engineering on complex relational, multi-source, or multi-channel datasets
• Experience analyzing marketing, advertising, campaign performance, audience, or customer engagement data and connecting model outcomes to business metrics
• Proficiency with software engineering and MLOps practices, including Git, code review, automated testing, modular design, documentation, CI/CD, model versioning, and monitoring

Benefits

• Potential base pay range: $91,300.00 - $228,200.00
• Incentive plans, bonuses, and/or other forms of compensation may be offered
• Range of health and welfare and/or other benefits