Principal Data Scientist – Deep Learning

🔥 12 hours ago

🇪🇸 Spain – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Jampp

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2013

📣 Marketing

🎮 Gaming

💰 $7M Series A on 2015-02

Marketing • Advertising • Gaming

Jampp is a programmatic advertising platform designed to drive growth for mobile apps through user acquisition and app retargeting. The company leverages machine learning and predictive algorithms to optimize advertising strategies, ensuring positive ROI by adjusting bids, placements, and creatives dynamically. Jampp offers full visibility into ad performance and spend via its dashboard and API. Its technology is used across various industries including gaming, commerce, and financial services, helping businesses achieve growth at scale by tapping into extensive ad requests and optimizing user engagement throughout the user journey.

📋 Description

• Define and drive the technical strategy for Jampp’s deep learning and embedding-based modeling architecture • Design, develop and iterate advanced DNN architectures for prediction and optimization, initially focusing on CPI/CPA use cases and expanding into real-time bidding, bid optimization, ranking and campaign optimization • Define strategies for learning rich representations of high-cardinality entities such as users, devices, creatives, publishers, advertisers, apps, campaigns and placements • Lead reusable embedding and representation-learning approaches across the platform • Improve the performance, scalability and generalization of machine learning models using raw signals and learned representations • Develop real-time prediction and decision-making models for programmatic advertising under strict latency and scale constraints • Evaluate modeling approaches and technologies for large-scale DSP and RTB systems • Establish technical standards and best practices for model development, experimentation, evaluation and productionization • Guide modeling initiatives from problem definition and experimentation through production deployment and continuous improvement • Design, code and deploy machine learning models and production tools, primarily in 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 performance, user response and business outcomes • Mentor and guide Data Scientists • Communicate technical findings, architectural decisions, trade-offs and recommendations to technical and non-technical stakeholders • Collaborate with Data Science and ML teams across Jampp and Affle on shared capabilities and machine learning solutions

🎯 Requirements

• Significant experience in Data Science, Machine Learning, Deep Learning or a closely related quantitative/technical role, with a track record of leading complex machine learning initiatives • Strong academic background in Computer Science, Applied Mathematics, Physics, Statistics, Engineering, Econometrics, or another quantitative field • Deep understanding of machine learning and deep learning fundamentals, including neural network architectures, representation learning, optimization and model evaluation • Extensive hands-on experience developing and deploying Deep Learning models in production environments • Strong experience with Python and the scientific/machine learning Python ecosystem • Experience working with large-scale datasets and high-cardinality categorical or ID-based features • Proven track record of taking machine learning models from experimentation and research through reliable production deployment • Experience designing or making significant technical contributions to ML architectures, training pipelines, model serving or other machine learning infrastructure • Experience working with real-time or latency-sensitive machine learning systems, ideally in advertising, marketplaces, recommendations or other high-throughput environments • Strong analytical and problem-solving skills • Strong technical communication skills • Experience providing technical leadership, mentorship or direction to other Data Scientists or engineers • Comfortable conducting daily professional communications in English (written and verbal) • Experience 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 a great fit