Sr Applied ML Data Scientist (Actuary)

Job Title: Sr Data Scientist, Applied ML

Location: Remote

About The Company

We are a leading financial services organization committed to innovation, integrity, and shaping the future of data-driven decision making. Our Data, Actuarial, and Finance teams partner to deliver scalable analytics, advanced modeling, and enterprise-grade data solutions that power strategic transformation initiatives. We foster a collaborative, high-performing culture where individuals are empowered to build impactful solutions that enhance operational efficiency, financial insight, and predictive capabilities.

Job Summary

We are seeking a technically skilled and business-savvy Senior Data Scientist for our Finance Transformation team to design, develop, and support advanced actuarial and financial applications. This role plays a critical part in transforming actuarial and finance functions by building scalable data pipelines, dashboards, and machine learning models that enhance decision-making and operational efficiency. The ideal candidate will partner closely with Actuarial, Finance, Data Science, and Technology teams to deliver solutions supporting reserving, pricing, predictive analytics, and financial reporting initiatives.

Key Responsibilities

Machine Learning, Engineering & Analytics Development:

• Design and optimize large-scale data pipelines using Python, PySpark, and SQL on cloud platforms such as Databricks and AWS. Develop, deploy, and support predictive models and actuarial tools for reserving, pricing, and financial planning & analysis. Automate manual actuarial and finance processes through structured data workflows and business logic integration.

Advanced Modeling & Statistical Analysis

• Apply statistical and AI/ML techniques including clustering, decision trees, ensemble methods, and neural networks. Perform data exploration, experimental design, feature engineering, and model validation to deliver production-ready analytical solutions.

Data Wrangling & ETL

• Ingest, cleanse, and structure financial and actuarial data from multiple systems (e.g., claims, billing, commissions). Perform fuzzy matching, data reconciliation, and quality validation checks to ensure integrity and completeness of modeling datasets.

Model Packaging & Deployment

• Collaborate with Data Engineers and MLOps teams to package, deploy, and monitor models in production environments. Implement new statistical methodologies as required for evolving business use cases.

Data Integration & Validation

• Establish robust data quality controls and reconciliation processes to support financial reporting and actuarial modeling accuracy.

Collaboration & Documentation

• Translate complex actuarial and finance requirements into scalable technical solutions. Maintain comprehensive documentation of code, model logic, data flows, and user instructions.

Requirements:

• Education:

• Bachelor’s or Master’s degree in Computer Science, Actuarial Science, Mathematics, Engineering, Finance, or related quantitative discipline (or equivalent experience).

• Experience:

• 6–8 years in data engineering, actuarial modeling, or analytics-focused technical roles

• 2+ years of hands-on machine learning modeling and development experience

• Experience within financial services or insurance environments preferred

• Skills:

• Strong programming skills in Python

• Proficiency in PySpark and SQL

• Experience with Databricks and/or AWS

• Solid understanding of statistical modeling and machine learning methodologies

• Experience with distributed computing and parallel ML processing

• Data wrangling expertise including fuzzy matching and regex techniques

• Business Intelligence tools experience (Tableau, Power BI, or similar)

• Understanding of pricing, reserving, or actuarial processes

• Knowledge of ML/AI libraries and frameworks

• Strong analytical mindset and attention to detail

• Certifications: Not required.

• Other:

• Comfortable working in agile or SAFe environments (Jira, Confluence a plus)

• Strong collaboration and communication skills

• Ability to operate effectively in evolving and ambiguous strategic environments

Preferred Qualifications:

• Experience building valuation, reserving, or pricing models for group insurance products
• Experience in agile or SAFe environments
• Experience partnering across matrixed organizations
• Strategic thinker with ability to translate business strategy into analytical execution
• Strong problem-solving skills and stakeholder engagement capabilities

Benefits:

• Competitive Compensation: Market-aligned salary and performance-based incentives.
• Health and Wellness: Comprehensive medical, dental, and wellness programs.
• Work-Life Balance: Hybrid work model with flexible collaboration.
• Professional Development: Opportunities to expand actuarial, ML, and cloud engineering expertise.
• Additional Perks: Exposure to enterprise transformation initiatives with high business impact.