Principal Data Scientist

About Vidmob

Vidmob is the creative data company. Its scoring software and analytics have become an essential ingredient in the creative and media decisions of the world’s largest marketers and agencies as they strive to drive business results through improved creative effectiveness.

As the leader in creative data, Vidmob’s influence lies in its partnerships and integrations across the digital ad ecosystem, its proprietary AI and machine learning models, and its ability to connect creative attributes to media performance across platforms, formats, markets, and business outcomes.

About the Role

We are seeking a Principal Data Scientist to serve as a technical leader for Vidmob’s most advanced AI, machine learning, and creative-performance intelligence initiatives. Reporting to the head of data science, you will sit at the intersection of computer vision, multimodal AI, causal inference, adtech measurement, and production-grade data products. There will be a strong focus on building recommendation systems.

This is a role for a deeply technical builder who can turn ambiguous business problems into rigorous modeling systems, production software, and measurable commercial value. You should be comfortable moving from research to prototype to deployed system, and you should bring enough computer science and engineering depth to build real systems and understand the underlying guts, not just use ML to do analyses.

As a senior individual contributor, you will not manage direct reports, but you will act as a standard-bearer and bar-raiser for the data science organization. Our global team interacts in English, so strong written and spoken English skills are essential.

What You’ll Do

• Develop recommendation models: Build systems using a portfolio of AI/ML tech that identifies likely drivers of underperformance, recommend creative improvements, prioritize edits, and support testing.

• Build creative intelligence systems: Develop models that connect visual, text, audio, structural, platform, and performance data. Turn those signals into diagnostics, scores, recommendations, and decision-support tools.

• Advance multimodal modeling: Work with video, image, text, audio, metadata, and KPI data. Apply computer vision, embeddings, LLMs, classification, clustering, and multimodal reasoning where useful.

• Raise analytical and modeling standards: Bring rigor to model design, validation, experimentation, inference, lift measurement, and business analysis. Design experiments, quantify uncertainty, and separate signal from noise using methods such as causal inference, hypothesis testing, regression, matched cohorts, and treatment/control analysis.

• Write production code: Contribute high-quality Python and SQL to data products, APIs, model pipelines, evaluation frameworks, and internal tools. Partner with Engineering to make systems reliable, observable, scalable, and maintainable.

• Improve ML infrastructure: Work with Engineering, Data Engineering, and DevOps on training pipelines, feature stores, model serving, evaluation harnesses, and deployment workflows across AWS and GCP / Vertex AI.

• Serve as an AI technical leader: Be a go-to expert on AI, machine learning, and measurement. Explain complex technical ideas clearly to product, engineering, commercial, executive, and customer-facing teams.

• Increase technical velocity: Help the organization adopt better tools, automation, evaluation methods, and AI-assisted development practices.

What You’ll Bring

• Advanced technical foundation: PhD in Computer Science strongly preferred. PhD in statistics, mathematics, natural science, engineering, operations research / management science, or economics also valued with strong CS and engineering experience. Exceptional MS candidates considered with substantial applied AI / ML experience.

• Senior applied AI / ML experience: 10+ years in applied data science, machine learning, or AI product development, or 5+ years post-PhD in a highly technical applied role.

• Strong engineering ability: Production-quality Python; experience designing data systems, reasoning about architecture, and partnering with engineers on deployed data products.

• Data querying and SQL writing involve the ability to extract information from large data tables and manipulate data using SQL queries and scripting code.

• Large-scale data and MLOps fluency: Hands-on experience with Databricks, BigQuery, Flink, or similar large-scale data platforms, plus model training, evaluation, deployment, monitoring, retraining, feature pipelines, experiment tracking, model registries, and ML observability.

• Applied ML depth: Strong command of supervised and unsupervised learning, model evaluation, feature engineering, embeddings, retrieval, ranking, clustering, classification, recommendation systems, and statistical validation.

• Multimodal / GenAI experience: Practical experience with computer vision, video/image understanding, LLMs, foundation-model workflows, or other applied GenAI systems.

• Advanced statistical and mathematical depth: Strong command of probability, statistics, optimization, experimental design, causal inference, hypothesis testing, confidence intervals, and treatment/control design.

• Business data context: Experience applying models to real business outcomes such as customer behavior, product usage, revenue, operations, marketing, marketplace dynamics, or risk. Adtech / martech experience is useful but not required.