Overview
Remote
Depends on Experience
Full Time
No Travel Required
Skills
python
spark
Staff Data Scientist
Real-Time Bidding (RTB)
Demand Side Platforms (DSPs)
Job Details
Staff Data Scientist
Location: Remote- CST time working hours
Salary: DOE
QUALIFICATIONS:
- Master's or PhD degree in a machine learning/AI related field such as engineering, statistics, computer science, or physics
- 5+ years of industry experience in data science, data analytics, and machine learning in advertising domain
- Extensive experience in Real-Time Bidding (RTB) systems. Skilled in applying advanced data science methodologies to improve the efficiency, profitability, and competitiveness of bidding strategies in programmatic advertising environments.
- Proficient in developing and implementing bid shading algorithms that strategically reduce bid prices while maintaining win rates
- Expertise in dynamic bid adjustment to ensure optimal budget pacing and delivery across campaigns. Adept at modeling for campaign success metrics, such as CTR, conversion rate, and CPA, to improve campaign ranking and ROI.
- Experienced in designing holistic bid strategies that align with multiple levels of objectives
- Deep domain knowledge in Demand Side Platforms (DSPs), Specialized in the architecture, algorithm design, and data pipelines powering DSPs, including real-time auction participation, budget management, frequency capping, and performance tracking.
- Deep experience in digital advertising including systems, measurement sciences, and principled incrementality approaches and passion for incentive challenges
- Advanced knowledge in predictive models such as parameterized methods, ensemble algorithms, deep neural network, and reinforcement learning algorithms such as multi-armed bandit algorithms, Q-learning, deep reinforcement learning
- Demonstrated experience using Python and Spark for big data query/processing and engineering skills for productionizing the solution
Experience with scientific computing libraries Scikit-learn, TensorFlow, PyTorch, and Spark ML-lib
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