Director Analytics

Select Portfolio Servicing•Salt Lake City, UT

About The Position

The Director Analytics leads the Analytics function through hands-on execution, business partnership, and AI-driven automation. This operational leader drives analytics strategy while writing complex code, modernizing legacy reporting, and ensuring data informs business operations. The role uses Claude AI, LLM workflows, and automated scripts to reduce manual work, automate reporting, and accelerate time to insight. The Director takes ownership of high-priority analytical deliverables, data pipelines, and executive requests; builds automated reporting and business workflows; and partners with operational and executive leaders to turn business problems into practical, decision-ready answers. The role leads and mentors analysts through technical standards, code reviews, and direct collaboration, while enabling the team to use AI tools effectively. It also modernizes reporting governance through standardized KPIs, authoritative metric definitions, reconciliation controls, self-service dashboards, and documented data logic and workflows.

Requirements

Bachelor's degree in Analytics, Finance, Operations, Computer Science, or a related quantitative field, or equivalent experience.
At least 7 years of hands-on experience in data analytics, business intelligence, or process engineering.
Hands-on proficiency with Claude AI and modern LLM platforms for query generation, SQL/Python debugging, document and data interpretation, and workflow acceleration.
Demonstrated experience automating manual business processes and reporting routines using Python, SQL, workflow platforms, or scripting tools.
Deep technical proficiency with relational databases and SQL, including complex joins, stored procedures, views, CTEs, and query performance tuning.
At least 4 years of hands-on experience designing, building, and deploying operational dashboards and reports.
Strong working proficiency in Python for data manipulation, process automation, and pipeline construction.
Proven experience leading technical team members while remaining actively engaged in technical deliverables.

Nice To Haves

Master's degree in a quantitative or business discipline.
Experience in mortgage servicing, financial services, lending, or portfolio analytics.
Experience integrating LLM APIs, custom prompts, or automation tools into business intelligence or operational workflows.
Track record of consolidating redundant legacy reporting suites into automated, streamlined self-service tools.

Responsibilities

Write complex SQL, develop Python scripts, model data, and teach others technical work.
Integrate Claude AI and modern LLM tools into daily analytical workflows for code development, query optimization, dataset analysis, and report generation.
Own execution of high-priority analytical deliverables, critical data pipelines, and ad-hoc executive requests.
Identify, design, and implement automated workflows to eliminate manual data gathering, repetitive processes, and spreadsheet-heavy operations.
Build end-to-end automated reporting pipelines that reduce manual touchpoints and deliver timely, reliable outputs.
Use Python automation, AI-assisted workflows, and platform schedulers to reduce manual maintenance and enable higher-value analysis.
Partner with operational leaders and executives to define business problems and deliver decision-ready answers.
Translate complex data into clear operational language for non-technical leaders.
Focus analytics work on business ROI, operational efficiency, and commercial impact.
Communicate status, data limitations, and trade-offs transparently to senior leadership.
Lead and mentor analysts by setting technical standards, conducting code reviews, and collaborating on major initiatives.
Train team members to use AI tools to increase productivity, debug code, and automate daily routines.
Shift the analytics team from reactive order-taking toward proactive business partnership.
Audit reporting environments, retire outdated reports, and replace manual processes with automated self-service dashboards.
Establish enterprise KPI frameworks, consistent metric definitions, and reconciliation controls.
Document data logic, automation pipelines, and operational workflows to preserve institutional knowledge.