Senior Data Scientist

Were hiring a Senior Data Scientist to lead end-to-end machine learning model development for core product area: growth monetization trust & safety recommendations etc.. Youll own the entire lifecycle: identifying opportunities building models shipping to prod and measuring impact. This is a hands-on IC role with high autonomy and accountability.

1. Discovery & Scoping

• Work with Product Eng and Analytics to uncover high-leverage ML opportunities
• Define problem statements success metrics and evaluation strategy upfront
• Perform exploratory analysis to assess feasibility and estimate ROI

2. Model Development

• Build features from structured unstructured data at scale: logs events text images time-series
• Select and implement the right approach: regression classification clustering deep learning LLMs causal models etc.
• Run rigorous offline experiments: cross-validation hyperparameter tuning error analysis

3. Deployment & Experimentation

• Partner with MLEs/DE to get models into production: real-time APIs batch pipelines edge
• Design A/B tests and interpret results. Make ship/no-ship calls based on data
• Build guardrails: latency fairness reliability and cost requirements

4. Production & Iteration

• Implement monitoring for feature drift prediction drift and performance decay
• Own model maintenance: retraining tuning deprecation
• Close the loop: use prod learnings to inform v2 v3 of the model

5. Org Impact

• Raise the bar: code reviews tech talks reusable tools documentation
• Mentor mid-level DS. Be the person others come to for ML design questions
• Evangelize data-driven decision making across the company

You Must Have:

• 5 years building ML models end-to-end with proven business impact. Youve shipped not just prototyped
• Fluent in Python and SQL. Strong grasp of numpy pandas scikit-learn. Experience with PyTorch or TensorFlow
• Solid ML theory: can explain regularization boosting embeddings and transformer basics without notes
• Experience with large datasets: Spark Presto BigQuery or similar. You know when to sample and when not to
• Track record of designing experiments and measuring incremental lift not just accuracy
• Product mindset: you care about users and business metrics as much as model metrics