Staff Data Scientist

About the Role

Seeking a skilled Staff Data Scientist to bridge the gap between technical expertise and strategic decision-making. This role is critical in driving actionable insights for Product, Engineering, and Leadership teams by leveraging advanced data science techniques and a deep understanding of business needs. You will lead the development of machine learning models, statistical analyses, and scalable solutions, while also guiding strategy and fostering a data-driven culture across the organization.

Key Responsibilities

• Evaluate machine learning opportunities to define data science strategies and align them with business objectives and product roadmaps, while communicating trade-offs and recommendations clearly to diverse stakeholders.
• Lead the end-to-end lifecycle of data science projects, including data exploration, feature engineering, model selection, validation, deployment, and optimization, ensuring scalability and reliability.
• Apply advanced statistical methods, such as Bayesian inference, hypothesis testing, time series analysis, and experimental design, to develop robust data-driven solutions for business challenges.
• Develop machine learning models using advanced techniques, including natural language processing, deep learning, and ensemble methods, while balancing accuracy, scalability, and explainability based on the use case.
• Use tools like dbt and Snowflake to clean, transform, and prepare data from various sources, maintaining data integrity and ensuring datasets are production-ready.
• Mentor and coach team members to promote technical excellence, foster collaboration, and build a data-informed culture that aligns with organizational goals.
• Define and implement best practices for machine learning and statistical analysis to enhance organizational capabilities and deliver robust, cost-effective solutions.

Required Experience

• A minimum of 7 years of experience in data science or machine learning roles, including hands-on expertise in building, deploying, and maintaining production-level models.
• Strong programming proficiency in Python, SQL, and machine learning libraries such as TensorFlow and scikit-learn, with the ability to write efficient, scalable code.
• Advanced knowledge of statistics, including Bayesian inference, hypothesis testing, experimental design, and time series analysis, with the ability to explain these concepts to both technical and non-technical audiences.
• Proven experience working with machine learning platforms such as SageMaker, Databricks, or Vertex AI, and leveraging these tools to deliver scalable solutions.
• Demonstrated ability to align data science initiatives with business objectives, define measurable success criteria, and communicate trade-offs in approaches to stakeholders.
• Expertise in breaking down complex data science problems into actionable steps, developing solutions, and prioritizing effectively to deliver measurable impact.
• Experience in analyzing trade-offs between individual models and ensemble approaches, considering factors such as accuracy, scalability, cost, and explainability.
• Exceptional verbal and written communication skills for translating technical concepts into actionable insights for diverse audiences, fostering alignment between technical teams and business goals.

Preferred Skills

• Advanced proficiency with data visualization tools such as Looker or Tableau to create compelling, actionable reports for stakeholders.
• Hands-on experience with data transformation tools such as dbt and Snowflake to ensure efficient, scalable data pipelines.
• Familiarity with advanced machine learning techniques, including reinforcement learning, generative adversarial networks (GANs), and other state-of-the-art methods.

Benefits Highlights

• Medical, dental, and vision insurance.
• Short-term and long-term disability insurance.
• Life and AD&D insurance.
• 401(k) plan.
• Paid vacation, sick leave, and holidays.
• Six weeks of paid parental leave.