Data Scientist

What Success Will Look Like

• Design, build, and optimize ML models and solutions - spanning traditional statistical ML, neural networks, and deep learning - to solve real-world problems in large-scale, real-time digital performance analytics.
• Develop innovative approaches for predictive modeling, anomaly detection, and automated alerting using time series and other complex datasets.
• Apply the full algorithm spectrum: from classical methods (regression, tree-based ensembles, clustering) to advanced deep learning architectures (transformers, LSTMs, CNNs) as the problem demands.
• Support AI initiatives including designing AI-driven alerts and contributing to AI roadmaps.
• Develop POC code and implement ML solutions that improve product capabilities - using AI coding tools (e.g., Claude, Cursor) natively to accelerate prototyping, code review, and iteration cycles.
• Process and analyze large-scale data using Ray and Spark.
• Collaborate with cross-functional engineering and product teams to integrate models into production systems.
• Stay current with industry trends in AI/ML and bring innovative approaches to the team.

Who You Are and What You've Done

• PhD in Computer Science, Statistics, or related field; or MS with 3+ years of applied ML/data science experience.

Strong foundation in

• Traditional ML: regression, classification, ensemble methods, clustering, dimensionality reduction
• Deep Learning & Neural Networks: CNNs, RNNs/LSTMs, Transformers, attention mechanisms
• Statistical modeling & algorithms: optimization, Bayesian inference, probabilistic modeling
• Proficiency in Python (primary), Scala, or similar languages for production-grade data science.
• Demonstrated ability to ship - from POC to scalable, integrated ML solution.
• AI-native developer: actively uses AI tools (Claude, Cursor, GitHub Copilot, or equivalent) in daily workflow for coding, debugging, and solution design - not just awareness, but habitual, productive use.
• Thrives in fast-paced, ambiguous environments and collaborates effectively across teams.

What Would Make You Stand Out (Bonus Points)

• Experience with real-time data processing and streaming analytics.
• Deep familiarity with time series analysis - forecasting, change-point detection, seasonality decomposition.
• Background in building AI-powered alerting, anomaly detection, or intelligent monitoring systems.
• Familiarity with cloud-based ML deployment and MLOps practices (model versioning, monitoring, retraining pipelines).
• Experience fine-tuning or integrating LLMs/foundation models into analytical or product workflows.

Pay and benefits

The expected salary range for this full-time position is $180,000 - $230,000 + equity + benefits. Compensation is determined by numerous factors such as your qualifications, experience, relevant education or training, and work location.

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