Director, Data Science
United States, no core hoursFull-TimeDirector
Salary243,800 USD per year
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Job Details
- Experience
- 10+ years of experience in data science, machine learning, or related fields, including 4+ years in a leadership or management role.
- Required Skills
- PythonSQLMachine LearningBigQueryRedshiftNLP
Requirements
- 10+ years of experience in data science, machine learning, or related fields, including 4+ years in a leadership or management role.
- Proven experience building and scaling multi-team roadmaps and adapting priorities in fast-moving environments.
- Strong ability to coach senior-level data scientists and elevate team performance through direct, constructive feedback.
- Expert-level proficiency in Python (or equivalent), with experience building and deploying ML/NLP systems in production.
- Advanced SQL skills with experience optimizing queries and working with large-scale columnar databases (e.g., BigQuery, Redshift, ClickHouse, Druid).
- Deep understanding of experimentation design, statistical modeling, and causal inference in product environments.
- Strong experience working with LLMs and AI systems, including evaluation methods (e.g., LLM-as-judge), fine-tuning, and applied AI experimentation.
- Ability to communicate technical tradeoffs clearly to executive and cross-functional audiences.
- Experience in consumer-facing technology environments where data is central to product strategy is highly preferred.
- Strong ownership mindset, with the ability to stay close to technical execution when needed.
Responsibilities
- Lead, mentor, and develop a team of experienced data scientists, setting clear expectations, providing actionable feedback, and enabling high-impact delivery.
- Oversee multiple concurrent data science initiatives spanning statistical modeling, experimentation design, and ML/NLP systems applied across core product areas.
- Partner with cross-functional teams to design, validate, and interpret experiments that inform product strategy and business decisions.
- Drive definition, monitoring, and improvement of key business metrics such as usage, acquisition, engagement, and revenue performance.
- Guide the evolution of data pipelines and analytical infrastructure to support scalable, privacy-conscious decision-making.
- Translate complex technical findings into clear, actionable insights for executive and cross-functional stakeholders.
- Foster an evidence-driven culture that prioritizes rigor, transparency, and high-quality decision-making.
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