Data Scientist
$150,000–$200,000 year
On-siteSuwanee, Georgia, United States
Job Summary
Design, develop, and optimize forecasting models for cash demand, labor, and operational workload using time-series and machine learning techniques. Implement LLM-based use cases for internal support and operations, applying prompt engineering and governance frameworks to integrate AI into production SaaS workflows. Establish data quality frameworks to detect anomalies and define validation rules for large transactional datasets, while partnering with engineering and product teams to deploy and monitor production-grade solutions. Lead complex forecasting initiatives and model risk management to ensure accuracy, explainability, and compliance across FinTech platforms.
Required Qualifications
- Must have unrestricted authorization to work in the U.S. without the need for employer sponsorship
- 6+ years of professional experience in data science, machine learning, or advanced analytics
- Advanced proficiency with Python and data science libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow/Torch)
- Strong SQL skills and experience working with messy, incomplete, high-volume operational data
- Well-rounded background in data science methods (e.g., supervised and unsupervised learning, anomaly detection, time series forecasting, survival analysis, simulation, optimization, causal analysis)
- Familiarity with metric design
- Demonstrated delivery of products that influenced business decisions
- Experience collaborating with engineering teams on model deployment and monitoring
- Proven ability to communicate complex concepts clearly and effectively
Desired Qualifications
- Experience in FinTech, banking, payments, retail cash management, or operations
- Experience identifying high-value data science opportunities in operational businesses
- Hands-on LLM development experience
- Familiarity with data quality and model governance frameworks
- Comfortable with ambiguity
- Driven to elevate themselves by elevating others
- Curious and lifelong learners
- Able to identify valuable problems before being asked
- Pragmatic rather than purely academically focused
- Capable of explaining very technical ideas to non-technical stakeholders
- Willing to challenge their own and others' assumptions with evidence
- Open to changing their mind when presented with new evidence
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