Data Scientist Jobs

About The Position

Requirements

• B.S. or M.S. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or a related technical field with 5+ years of relevant industry experience; OR a Ph.D. in a related field with 2+ years of relevant experience.
• Demonstrated ability to independently own the full data science lifecycle — from problem formulation and feature engineering through model deployment, monitoring, and ongoing maintenance.
• Solid expertise in several core areas of machine learning and/or statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference, experimentation design), with the judgment to select appropriate methods for complex problems.
• Strong foundation in probability and statistics, including techniques that scale to large datasets.
• Experience designing and analyzing experiments (e.g., A/B testing) and building robust model and experiment validation frameworks.
• Strong Python and SQL skills; experience with ML frameworks such as TensorFlow or PyTorch.
• Ability to write efficient, modular, well-tested code and to collaborate with engineering to move models and analyses into production.
• Strong communication skills, including the ability to convey complex technical concepts to both technical and non-technical audiences.

Nice To Haves

• Experience developing, evaluating, or optimizing models or bidding algorithms for RTB environments.
• Experience working with a cloud platform like GCP/AWS/Azure, with emphasis on GCP and the Vertex AI platform.
• Experience with ML pipeline and orchestration tools such as TFX, Kubeflow, or Airflow.
• Familiarity with other programming languages such as Java and Go.
• Experience working in digital media, marketing technology, or advertising technology, especially in marketplace, auction, or exchange systems.
• Experience supporting and improving production ML models beyond their initial deployment.
• Experience mentoring junior data scientists.

Responsibilities

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
• Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems.
• Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
• Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.
• Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.
• Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
• Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.
• Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes.
• Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.
• Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.
• Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.
• Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise.
• Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).
• Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences.
• Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

Benefits

• medical
• dental
• vision
• 401k
• equity
• bonus