Staff Data Scientist

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Requirements:

• Mastery of Data Science Stack: Fluent in Python/R and SQL, with deep expertise in data science libraries (e.g. scikit-learn, pandas). Familiar with MLOps and transitioning models to production.
• Modeling Expert: Proficient in classical statistical methods and state-of-the-art machine learning techniques.
• Big Data Proficiency: Comfortable with large-scale data technologies (e.g. Spark) and scaling data processing pipelines.
• Statistical Rigor: Strong foundation in statistics, capable of designing and analyzing experiments.
• Data Storytelling: Ability to translate complex analyses into clear narratives through visualization.
• Project Leadership: Proven track record of leading complex data science initiatives delivering business value.
• Bar Raiser: Passionate about mentoring junior data scientists and improving team technical standards.
• Product-Driven Mindset: Ability to balance speed with quality in fast-paced environments.
• Engineering Best Practices: Knowledgeable in software engineering principles like version control and testing.
• Influence Without Authority: Effective communication and influence across various organizational levels.
• Clear Communication: Skillful at simplifying complex technical concepts for diverse audiences.
• Effective Prioritization: Proficient in managing multiple priorities, focusing on high-impact work.

Responsibilities:

• Architecting Data Science Solutions: Lead the conceptualization and planning of data science initiatives that align with and drive key business objectives.
• Solving Complex Data Challenges: Spearhead the development and implementation of sophisticated data models and algorithms, diving into hands-on coding for the most critical components.
• Elevating Team Standards: Guide the technical direction of the team through thoughtful code reviews and targeted mentorship, fostering a culture of excellence and continuous improvement.
• Shaping Product Strategy: Collaborate closely with product managers to identify opportunities where data science can drive product innovation and user value.
• Influencing Business Decisions: Synthesize complex analyses into actionable insights, presenting compelling data-driven narratives to inform senior leadership's strategic decisions.
• Advancing Technical Capabilities: Evaluate and implement cutting-edge data science methodologies and tools, ensuring the team stays at the forefront of the field.
• Optimizing Data Infrastructure: Partner with data engineers to enhance data pipelines, logging systems, and MLOps practices, creating a robust foundation for advanced analytics and modeling.
• Cultivating Data Science Knowledge: Author technical documentation and thought leadership pieces to disseminate key insights, best practices, and learnings across the organization.