Abacus is looking to hire a Senior Data Scientist in the Austin, TX area.
Role Summary
As a Senior Data Scientist, you will design, develop, and deploy advanced machine learning models to solve complex business problems. You will collaborate closely with cross-functional teams, transform large and messy datasets into actionable insights, and ensure that our models deliver sustained value. Your expertise will shape how we leverage AI to enhance product returns management and operational decision-making.
Key Responsibilities
- Data Acquisition & Preparation: Gather, clean, and process structured and unstructured data from multiple sources to ensure quality and readiness for analysis.
- Model Development: Design, build, and optimize machine learning models (regression, classification, neural networks, etc.) to address business challenges.
- Pipeline Engineering: Develop, automate, and maintain robust data and ML pipelines to streamline model training, testing, and deployment.
- Model Maintenance: Monitor, retrain, and refine deployed models to ensure performance stability and adaptability over time.
- Performance Analysis: Evaluate model effectiveness using statistical and ML metrics, providing actionable recommendations for improvement.
- Business Partnership: Collaborate with stakeholders to translate complex business problems into data-driven solutions and experimental designs.
- Analytical Reporting: Deliver clear, concise analyses and reports for both technical and non-technical audiences, including ad hoc and recurring business requests.
- Documentation & Transparency: Maintain thorough documentation of methodologies, assumptions, and results to ensure reproducibility and traceability.
- Innovation & Best Practices: Stay current with advancements in AI, ML, and data science to integrate emerging methods and tools into workflows.
- Culture Building: Promote a culture of accountability, openness, and continuous learning within the data science team.
- Will Serve as a backup to the CIO
Required Skills & Experience
- Education & Experience:
- 5+ years of related experience with a Bachelor’s degree, or 3+ years with a Master’s degree; or equivalent work experience.
- Degree in a quantitative field (e.g., Statistics, Data Science, Applied Mathematics, Economics, Engineering, Computer Science).
- Technical Expertise:
- Proficiency in Python (pandas, numpy, scipy) and SQL for data manipulation, analysis, and pipeline development.
- Experience with machine learning frameworks and techniques, including regression, classification, neural networks, and feature engineering.
- Experience with Azure ML, Power BI, or similar analytics platforms.
- Skilled in web scraping, handling large-scale multi-source datasets, and managing ambiguous data environments.
- Strong knowledge of statistical methods for experimentation (e.g., randomization, t-tests, confidence intervals).
- Analytical & Business Skills:
- Proven track record of managing and delivering high-volume analytics or experimentation projects across multiple stakeholders.
- Ability to translate analytical insights into actionable business recommendations.
- Communication & Collaboration:
- Excellent written and verbal communication skills, comfortable presenting to both technical and business audiences.
- Strong documentation, organizational, and time management skills.