Director - Actuarial Process Management & Analytics

Role Overview:
This is a mission-critical role that bridges the gap between raw data engineering and high-stakes actuarial decision-making. You will be responsible for the end-to-end integrity of the actuarial data ecosystem, ensuring that the "fuel" for our pricing, valuation, and risk models is accurate, timely, and compliant with defined data standards. Lead the advanced analytics initiatives that transform these high-quality datasets into strategic business insights.

Key Responsibilities:

• Business Requirements & Metadata Management: Define and document business requirements, maintain and enrich actuarial business glossaries, data dictionaries, translate the requirements to data requirement ensuring all data used is appropriately reviewed, documented, and disclosed
• Data Quality Governance: Lead the design and implementation of automated data quality (DQ) frameworks. Define and monitor DQ KPIs such as completeness, accuracy, and reasonability
• Automated Controls & Reconciliation: Design and oversee data controls and automated reconciliation processes to proactively catch anomalies.
• Reporting and Data Visualization: Design and implement reporting and data visualization framework for delivering on-time and up-to-date insights to our stakeholders, using tools like Databricks AI/BI, PowerBI
• Advanced Analytics: Drive the development of modern analytics solutions, including predictive modeling and trend analysis, utilizing Python, R, and SQL to solve complex business problems.
• Stakeholder Management: Serve as a Technical Liaison for multiple stakeholders, managing expectations and supporting key projects like quarterly close cycle, policy admin conversion efforts etc.
• Root Cause & Remediation: Lead deep-dive investigations into data defects, perform root-cause analysis and partner with data team to implement permanent structural fixes.
• Team Mentorship: Lead and develop a high-performing team of data analysts and actuaries, fostering a culture of technical excellence and innovation.

Core Technical Skills:

• SQL & Advanced Querying: Mastery is mandatory for data retrieval and manipulation.
• Data Visualization: Expertise in tools like Power BI, Databricks AI/BI, Tableau to build scalable, self-serve dashboards.
• Programming (Python/PySpark): Essential for reproducible workflows, automation, and advanced statistical modeling (e.g., Pandas, NumPy).
• AI & ML Literacy: Understanding of machine learning basics (e.g., predictive modeling, A/B testing) and integration of Generative AI for productivity (e.g., drafting queries, summarizing insights).
• Cloud Platforms: Familiarity with modern data stacks (Databricks preferred)

Strategic & Soft Skills:

• Data Storytelling: The ability to translate complex technical findings into clear, actionable narratives for non-technical stakeholders.
• Business Acumen: Linking analytical outputs directly to organizational goals, such as revenue growth, churn reduction, or operational efficiency.
• Stakeholder Management: Experience with managing multiple stakeholders’ expectations, help define priorities and resolve conflicts when competing priorities arise
• Data Governance: Ensuring data quality, security, and ethical compliance (e.g., GDPR, CCPA) across all reporting.

Qualifications:

• Bachelor’s degree or higher in data science, Computer Science, Statistics, Business Analytics, Finance, or related field
• 7–10 years of experience in data analytics, business intelligence, or reporting strategy, with at least 3 years in a leadership role
• Proven track record of defining and communicating KPIs at the executive level
• Excellent leadership and communication skills with the ability to translate data into business insights
• Demonstrated success in leading cross-functional teams and collaborating with Finance and IT.