Analyst, Investment Analytics

About Us

NLG Capital, the investment management company of NLG, manages approximately $40+ billion in AUM. Our purpose is simple – to allow NLG to keep its promises. How we do this – an obsessive focus on delivering investment results. We drive results through a rigorous but collaborative investment process, and we hold ourselves to the highest of standards, both as investors and teammates. While our process is based on rigor, our culture is based on our guiding principles: Meritocracy, Uncompromising Transparency, Intellectual Honesty, Performance Driven Learning, Relentless Pursuit of Excellence. These principles serve as the foundation of our team. They govern the way we conduct ourselves, how we interact with teammates and how we approach every investment decision. We act with the integrity and humility required to make the investment decision that is right for the firm, and ultimately our policyholders. Our culture has been deliberately built to foster curiosity and provide opportunities to learn and grow, both as people and investors. The flat and collaborative nature of our team promotes an atmosphere where the best ideas are continually expressed and rewarded. Our pursuit of excellence is unwavering.

Please note that we do not offer visa sponsorship for this position.

Position Summary

The Investment Analytics Analyst will have direct exposure to NLG Capital's investment process by supporting senior members of the Office of the CIO team. The role works across asset allocation, risk management, credit surveillance, and competitive intelligence — using modern data including AI tooling to bring transparency, objectivity, and speed to the way we frame portfolio positioning and risk.

This is a high-impact seat in National Life's investment process. The analyst will provide the information and reporting our decision-makers need to act with conviction, and will help ensure those decisions remain aligned with the strategic objectives and constraints facing a life insurance general account.

Responsibilities

• Support the Office of the CIO in bringing transparency to NLG Capital's investment strategies across the general account.

• Build and maintain analytical tools, monitors, and dashboards that organize complex datasets — issuer-level credit data, manager exposures, peer disclosures, market data — into legible and actionable reports for senior decision-makers.

• Automate recurring workflows — risk reporting, exposure aggregation, ALM scenario inputs — so the team spends its time on judgment, not assembly.

• Support investment, risk, and operations colleagues in identifying the highest-leverage problems where technology and AI can compress turnaround time or expand coverage.

• Apply software engineering practices — version control, code review, testing, documentation — so the tools we build are durable and shareable across the team.

The Person

Pivotal Experience & Expertise

• Degree in Financial Engineering, Data Science, or Computer Science (or a combination of experience and education), with a demonstrated interest in financial markets.

• Demonstrated experience in financial analysis, analytics, or quantitative development roles considered a plus.

• Python and SQL are core requirements, not nice-to-haves.

• Proficiency with Excel and at least one BI / visualization platform (Tableau, Power BI, or equivalent).

• Practical experience using LLMs and AI coding assistants (e.g., Claude, Claude Code, Copilot, Cursor) to accelerate analysis, process unstructured documents, and build internal tooling. We expect candidates to have opinions about where these tools work, where they fail, and how to verify their output.

• Exposure to a cloud data platform (Snowflake, Databricks, BigQuery, or similar) is a plus.

• Experience with code development best practices — modular code, testing, documentation, code review — is a plus.

• Intense intellectual curiosity, strong work ethic, and a track record of driving projects to completion.

• Solid interpersonal skills and an inclusive, collaborative working style.

Cultural Fit

• Results-driven, with a bias toward shipping useful tools rather than perfect ones.

• Treats AI as a force multiplier — neither dismissive nor over-credulous. Knows when to use it, when to verify, and when a model is the wrong tool for the job.

• Comfortable in an organization that learns from both its mistakes and its successes.

• Comfortable with ambiguity, and willing to own the work of resolving it.

• Skills & Tags

  pythongoaiiosdataanalytics