Senior Data Scientist

About the Role

Axial Search is a specialist executive search firm focused on placing leaders who help organizations navigate AI transformation. We track thousands of AI transformation roles and provide market insights to candidates.

We've tracked 13,400+ specialist-level data science postings across the US in the last six months. Hiring concentrates in technology hubs like California, New York, and Washington, with strong secondary demand across financial services, professional services, and healthcare. Specialist-level data scientists in this cohort command salaries between $160k and $310k. The strongest candidates combine deep technical fluency in statistical modeling and machine learning with the ability to translate business problems into data-driven solutions, ship production systems, and partner effectively across engineering and product teams.

Responsibilities

• Own end-to-end data science projects—from problem definition and exploratory analysis through model development, validation, and deployment into production systems
• Build and maintain machine learning models that drive measurable business impact, including feature engineering, model selection, and performance optimization
• Partner with engineering and product teams to integrate models into applications and data pipelines, ensuring scalability and reliability
• Design and execute experiments (A/B tests, causal inference studies) to validate hypotheses and inform product and business decisions
• Communicate findings and recommendations to non-technical stakeholders, translating complex analyses into actionable insights
• Lead or contribute to data infrastructure improvements, working with engineers to reduce model training time, improve data quality, and streamline workflows
• Mentor junior team members and contribute to data science best practices across the organization

Requirements

• 5–8 years of professional data science experience, including hands-on work building and deploying production machine learning models
• Strong foundation in statistics, experimental design, and machine learning fundamentals; fluency in Python or R and SQL
• Demonstrated ability to work across the full model lifecycle—from scoping and feature engineering through validation, deployment, and monitoring
• Proven track record shipping models or analyses that directly influenced business outcomes or product decisions
• Clear communication skills; ability to explain technical work and uncertainty to both technical and non-technical audiences