Senior Data Scientist

Job Description

Job Description

Abacus is looking to hire a Senior Data Scientist in the Austin, TX area.

This is a high impact role where the Senior Data Scientist will act as the expert for the entire data science workflow. The person in this role will manage everything from data collection and exploration to model development, deployment, and long-term monitoring. They will work closely with business leaders, engineers, and industry experts to create predictive models, algorithms, and probability engines that drive real results.

Beyond deployment, the candidate will manage the post-deployment lifecycle, validate real-world model performance and translate insights into actionable, compelling recommendations. They will conduct Root Cause & Corrective Action (RCCA) analysis to diagnose model shortcomings and design forward-looking roadmaps to continually improve system performance. This role requires both strategic thinking and hands-on technical execution to ensure that AI / ML solutions drive measurable business impact.

Ideal Candidate

The ideal candidate is malleable, experimental, and unafraid to innovate. They proactively explore new ideas, demonstrate initiative, and bring energy to solving complex problems using cutting-edge AI / ML techniques. They understand how to balance business needs with data science best practices and can design thoughtful workflows that support both.

The ideal candidate is a continuous learner with a strong appetite for new technologies and a deeply collaborative mindset. They excel at communicating with cross-functional teams and executive stakeholders and thrive in a fast-paced environment with shifting priorities. Above all, they bring clarity to ambiguity and help shape the direction of high-value AI initiatives.

Key Responsibilities

• Solve business and customer challenges using advanced AI / ML techniques.
• Build prototypes and scalable AI / ML solutions to be integrated into production software products.
• Collaborate with software engineers, business stakeholders, and product owners in an Agile environment.
• Take full ownership of model outcomes and drive continuous improvement across the model lifecycle.
• Lead end-to-end application and feature development with a production-ready mindset.
• Review code, provide constructive peer feedback, and uphold high engineering standards.
• Guide and mentor a team of data scientists on technical, methodological, and project-related matters.
• Engage professionally with business partners, external partners, and key internal contacts.
• Translate complex quantitative findings into actionable insights for both technical and non-technical audiences.
• Break down ambiguous business questions into clear, structured, data-driven problem statements.
• Stay informed on emerging ML / AI research and leverage internal learning resources to innovate.
• Collaborate with other data science and machine learning teams to foster a strong data science culture.
• Maintain clear, thorough, and up to date documentation for models, workflows, and processes to ensure transparency and knowledge sharing

Qualifications