Data Science Engineer (Finance)

Responsible for end-to-end data science solutions including data infrastructure, model development, and production deployment. Combines analytics engineering, data science, and ML engineering capabilities to deliver complete solutions that drive business value.

Essential Postions Responsibilities - This is a salary exempt position.

Advanced Analytics Engineering. Design and implement complex ML-focused data transformation pipelines that serve multiple business functions, extending business data marts created by Data Engineers. Build reusable feature engineering models and maintain comprehensive documentation standards. Create and maintain AI semantic layers enabling LLM data access across the enterprise. Establish data governance and quality frameworks for ML applications that ensure reliable, trustworthy data assets.

Collaboration with Data & Analytics Team. Collaborates closely with Data Engineers to leverage existing data infrastructure and business data marts as the foundation for ML-focused analytics. Builds specialized feature engineering pipelines and AI-ready semantic layers that extend beyond traditional BI reporting to enable predictive and prescriptive analytics. Collaborates with Data Engineers to identify data quality requirements for ML applications and coordinates on compute resource allocation for ML workloads.

Applied Data Science. Lead complete data science projects from analysis through deployment, working independently on moderately complex problems. Develop statistical models and machine learning algorithms to solve business problems. Conduct advanced exploratory analysis and hypothesis testing to uncover actionable insights. Design and execute experiments to validate model performance and business impact.

MLOps & Production Engineering. Deploy models to production using appropriate frameworks and APIs, ensuring scalability and reliability. Implement model monitoring, alerting, and automated retraining processes. Build scalable model serving infrastructure and maintain production systems. Integrate ML solutions with business applications and existing data infrastructure.

Collaboration and Communication. Partner with business stakeholders to understand requirements and translate them into technical solutions. Collaborate with senior team members on complex projects and provide guidance to junior staff. Present findings and recommendations to stakeholders in clear, actionable formats. Support cross-functional projects and maintain effective working relationships.

Technical Experience: 3-5 years with proven track record in data engineering, analytics, and machine learning. Expert-level SQL and Python/R skills with experience in multiple ML frameworks. Experience with MLOps tools, cloud platforms, and production model deployments. Strong knowledge of data architecture, dimensional modeling, and semantic layer technologies. Experience differentiating between BI-focused and ML-focused data modeling approaches. Familiarity with containerization (Docker), CI/CD pipelines, and software engineering best practices. Experience with data visualization and business intelligence tools. Strong problem-solving skills and ability to work independently on complex projects.

Industry Experience: None

Minimum Education: Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, or a related field or 4 years related experience.

Preferred Education: Master's degree in related field or advanced certifications in cloud platforms and data science tools.

We value military experience and welcome veterans to join our team.

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