Principal Data Scientist – Deep Learning

This is a fully remote position, open to applicants in Spain.

📋 Description

• Define and steer the technical strategy for Jampp’s deep learning and embedding-based modeling framework.

• Design, develop, and refine advanced DNN architectures for prediction and optimization, initially targeting CPI/CPA use cases and broadening into real-time bidding, bid optimization, ranking, and campaign optimization.

• Establish strategies for learning rich representations of high-cardinality entities such as users, devices, creatives, publishers, advertisers, apps, campaigns, and placements.

• Lead the development of reusable embedding and representation-learning methodologies across the platform.

• Enhance the performance, scalability, and generalization of machine learning models utilizing raw signals and learned representations.

• Create real-time prediction and decision-making models for programmatic advertising while adhering to strict latency and scale constraints.

• Assess modeling methods and technologies for large-scale DSP and RTB systems.

• Set technical standards and best practices for model development, experimentation, evaluation, and productionization.

• Guide modeling projects from problem identification and experimentation through to production deployment and ongoing improvement.

• Design, implement, and deploy machine learning models and production tools, primarily utilizing Python.

• Collaborate with ML Engineers, Data Engineers, and Software Engineers on training infrastructure, data pipelines, feature infrastructure, serving architecture, and feedback loops.

• Analyze model and product performance metrics and their impact on bidding decisions, campaign effectiveness, user engagement, and business outcomes.

• Mentor and support Data Scientists.

• Communicate technical insights, architectural choices, trade-offs, and recommendations to both technical and non-technical stakeholders.

• Collaborate with Data Science and ML teams across Jampp and Affle on shared capabilities and machine learning solutions.

⛳️ Requirements

• Extensive experience in Data Science, Machine Learning, Deep Learning, or a closely related quantitative/technical role, with a proven history of leading complex machine learning projects.

• Strong academic qualifications in Computer Science, Applied Mathematics, Physics, Statistics, Engineering, Econometrics, or another quantitative discipline.

• Comprehensive understanding of machine learning and deep learning principles, including neural network architectures, representation learning, optimization, and model evaluation.

• Significant hands-on experience in developing and deploying Deep Learning models in production settings.

• Proficient in Python and the scientific/machine learning Python ecosystem.

• Experience working with large-scale datasets and high-cardinality categorical or ID-based features.

• Demonstrated success in taking machine learning models from experimentation and research to dependable production deployment.

• Experience in designing or making substantial technical contributions to ML architectures, training pipelines, model serving, or other machine learning infrastructures.

• Experience with real-time or latency-sensitive machine learning systems, preferably in advertising, marketplaces, recommendations, or other high-throughput environments.

• Strong analytical and problem-solving abilities.

• Excellent technical communication skills.

• Experience providing technical leadership, mentorship, or guidance to other Data Scientists or engineers.

• Proficient in conducting daily professional communications in English (both written and verbal).

• Familiarity with embeddings, representation learning, DSPs, programmatic advertising, RTB, ad exchanges, ad networks, recommendation systems, pricing, ranking, personalization, fraud detection, ML platforms, training pipelines, feature stores, model serving, or monitoring systems is highly desirable.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional growth and development.

• Flexible working hours and remote work options.

• Comprehensive health and wellness benefits.

• Collaborative and innovative work environment.