Senior Machine Learning Scientist

About the job

Responsibilities

• Own end-to-end ML solutions within your domain, from problem framing and metric design through data exploration, model development, deployment, and post-launch iteration.
• Define technical direction for your area, including model architecture, system design, data contracts, and integration patterns with existing services.
• Lead multi-quarter ML initiatives in partnership with engineering, product, and business stakeholders, driving projects from ambiguous requirements to production systems at scale.
• Author technical blueprints and system designs that clearly outline objectives, constraints, and trade-offs for complex ML systems.
• Design and implement production-grade ML models (e.g., gradient-boosted trees, deep learning, optimization algorithms, bandits/RL policies) that operate reliably under real-world constraints.
• Build robust training, evaluation, and serving pipelines with embedded observability, drift detection, and failure handling across the ML lifecycle.
• Enhance experimentation and measurement strategies, including A/B tests, causal inference methods, and long-horizon metrics.
• Partner with engineering teams to translate ML designs into scalable, maintainable production systems.
• Influence domain roadmaps by connecting ML opportunities to business objectives, articulating trade-offs, and building stakeholder alignment.
• Translate ambiguous business problems into clear ML formulations with measurable success criteria.
• Lead structured reviews with cross-functional partners, presenting complex technical concepts and trade-offs.
• Raise the technical bar for the broader science community by codifying best practices, experimentation standards, and reusable patterns.
• Mentor other data and machine learning scientists, providing technical guidance.
• Drive adoption of AI best practices.

Requirements

• Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or related quantitative field, or equivalent industry experience.
• 6+ years (Master’s) or 4+ years (PhD) of hands-on experience applying machine learning to real-world problems.
• Demonstrated track record of leading at least one complex, multi-stakeholder production ML initiative that delivered measurable business impact.
• Deep ML expertise in supervised and unsupervised learning, including tree-based methods, generalized linear models, and/or deep learning.
• Strong feature engineering, regularization, calibration, and error analysis skills.
• Strong experimentation and statistics skills: designing and interpreting A/B tests, understanding bias/variance and statistical power, and applying causal inference techniques.
• Fluency in Python and core data/ML libraries (pandas, NumPy, scikit-learn, PyTorch or TensorFlow).
• Solid software engineering practices (clean code, testing, version control, code review).
• Proficient with large-scale data: strong SQL skills and familiarity with distributed data processing (e.g., Spark, Hive).
• Proven ability to lead through influence: aligning cross-functional stakeholders on problem definitions, success metrics, and rollout plans.
• Strong communication skills: articulating technical concepts, trade-offs, and recommendations clearly to both technical and non-technical audiences.
• Experience with complex system diagnosis: combining logs, metrics, experiments, and domain intuition to identify root causes and drive data-informed remediation plans.
• Experience with ads, auctions, marketplace optimization, or bidding systems is preferred.