Principal Data Scientist - AI, Machine Learning, Fraud Detection & Risk ModelingPrincipal Data Scientist, Artificial Intelligence (AI), Machine Learning, Fraud Detection, Risk Modeling, Generative AI, Large Language Models (LLMs), Predictive Analytics, MLOps, Python, SQL, Snowflake, Databricks.Enjoy the flexibility of working remotely while living in one of our approved hiring states: Georgia, South Carolina, North Carolina, Florida, or Tennessee.Job SummaryThe Principal Data Scientist is a senior technical leader who drives advanced machine learning and data science initiatives across the organization. This role combines hands-on technical expertise with strategic influence. The Principal Data Scientist owns end-to-end delivery of complex ML projects, presents recommendations to leadership, leads technical discussions, and serves as the recognized authority on modeling approaches. The role partners cross-functionally with Product, Engineering, Finance, and Marketing to define and solve high-impact business problems, with particular focus on fraud detection, customer intelligence, and risk modeling.
Core Responsibilities- Own end-to-end delivery of strategic, high-impact data science projects from problem definition through production deployment, monitoring, and optimization. Lead scoping discussions and present project results as subject-matter authority.
- Develop and deploy advanced machine learning models for fraud detection, risk assessment, anomaly detection, customer segmentation, and churn prediction. Build real-time scoring pipelines, ensure production reliability, and present model design choices as the authority on their technical merits.
- Lead fraud detection initiatives, designing detection algorithms, collaborating with compliance and investigation teams, and continuously improving model performance as fraud patterns evolve.
- Evaluate and apply emerging AI and machine learning technologies, including large language models (LLMs), generative AI, and agentic systems, to enhance product capabilities and competitive advantage.
- Define and document best practices for model development, testing, and deployment, and present them to stakeholders as the authority on the subject.
- Design and build scalable data pipelines, feature engineering frameworks, and reusable modeling infrastructure that accelerate model development and deployment across the organization.
- Monitor model performance, data quality, and business outcomes in production. Identify degradation, troubleshoot issues, drive continuous improvements, and present findings while leading discussions on remediation priorities.
- Partner with Product, Engineering, Finance, and Marketing teams to define analytical requirements, establish success metrics, and deliver recommendations that drive strategic decision-making.
- Present complex analytical findings and strategic recommendations to executive leadership and business stakeholders with clarity and confidence. Communicate technical concepts to diverse audiences and drive adoption of data-driven strategies.
- Ensure all models and analytical processes comply with regulatory, privacy, security, and governance requirements. Collaborate with compliance and legal teams as needed.
- Stay current on emerging data science, ML, and AI trends. Identify, prototype, and recommend innovations that improve customer experience and competitive position.
- Contribute to documentation, best practices, architectural decisions, and reusable code libraries that strengthen the organization's data science capabilities.
Your Impact in This Role- Successful deployment and adoption of advanced ML models that generate measurable business impact (fraud reduction, conversion improvement, risk mitigation, operational efficiency), with results presented clearly to leadership.
- Demonstrated ability to identify and execute high-impact strategic initiatives independently with minimal direction or coaching, and to lead discussions that align stakeholders on priorities.
- Clear, confident communication and presentation of complex technical concepts to executives and stakeholders; proven ability to lead discussions, serve as the authority in the room, and influence decisions.
- Improved data science processes, tooling, and infrastructure that reduce time-to-model and accelerate deployment cycles.
Required QualificationsEducation & Experience:
- Master's or PhD degree in a quantitative field (Data Science, Computer Science, Statistics, Mathematics, Physics) or equivalent 8+ years of hands-on industry experience as a Data Scientist, Machine Learning Engineer, or similar role, with experience presenting complex work to senior audiences.
- Demonstrated success leading multiple end-to-end machine learning projects from ideation through production deployment and monitoring, including presenting outcomes and leading project discussions with stakeholders.
- Proven track record of self-initiated, high-impact projects executed with full ownership and minimal oversight.
Skills & Competencies:- Very high proficiency in multiple machine learning methods: supervised learning, unsupervised learning, and deep learning. Able to lead method-selection discussions as the subject matter expert.
- Advanced expertise in SQL and Python; strong software engineering fundamentals including version control, testing, code design, and distributed systems concepts.
- Strong proficiency in AI technologies, including experience evaluating, fine-tuning, or deploying large language models (LLMs) and generative AI systems in production.
- Strong experience with modern ML operations (MLOps), model deployment, monitoring, and serving in production environments; hands-on familiarity with cloud platforms (Snowflake, Databricks, AWS, GCP, or Azure).
- Strong understanding of statistical analysis, experimentation design, A/B testing, hypothesis testing, and causal inference methodologies.
- Experience building real-time or near-real-time scoring systems and production ML pipelines.
- Excellent communication skills: ability to explain complex technical concepts clearly to both technical and non-technical audiences. Strong presentation skills and ability to lead discussions without heavy coaching.
- Strong cross-functional collaboration and stakeholder management skills. Comfortable influencing decisions and driving adoption across Product, Engineering, Finance, and business teams.
Preferred Qualifications:- Experience in financial services, tax preparation, SaaS, fintech, or e-commerce industries.
- Hands-on experience building fraud detection, anomaly detection, or risk modeling systems.
Physical & Work Environment Requirements- This is a fully remote role (US-based only).
- Occasional travel within US is required for team collaboration, strategy sessions, and business meetings (estimated 4-6 times per year).
- Work is performed in a standard office environment with remote flexibility.
- Must be able to work at a computer for extended periods.
What we offer:At TaxSlayer we realize the most important aspects in leading our industry are the bright minds behind everything we do. We are proud to offer our employees a highly competitive, comprehensive, and flexible benefits program.
- We offer flexible remote and/or hybrid work options when possible, depending on the role.
- Exempt Salaried employees receive a generous PTO bank, in addition to Paid Holidays.
- Insurance: Medical, Dental, and Vision insurance offerings from Aetna and SunLife. Options for Employee Only, Employee + Spouse/Domestic Partner, Employee + Children, or Family.
- 401K with 150% match on up to 3% contribution
- Performance-based bonus and salary review process
- Other offerings: Wellness Program, Life Insurance for employee, Long-term and Short-term disability, Critical Illness Insurance, Accident Insurance, Education Assistance, company-paid parking, on-site fitness facility, and a company store.
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Please note: As a federal contractor, we are responsible to ensure our employees meet any obligations set forth by the U.S. government. We will inform you of any applicable requirements as they arise.
This job description is not intended to be all-inclusive and may be subject to change to meet business needs. 
