NM Capital - Investment Data Analyst

Northwestern Mutual•Milwaukee, WI

8d•$75,600 - $130,440•Onsite

About The Position

This role will focus on the data associated with NMC’s investment transaction lifecycle; including deal sourcing, due diligence, valuation, portfolio monitoring, portfolio performance, and reporting to clients. Manage, transform, and integrate portfolio and benchmark data to support advanced quantitative analysis, ensuring information is accurate, auditable, and analytically robust. Develop and maintain investment performance measurement frameworks, including returns, benchmarks, attribution, and performance drivers at the asset, strategy, and portfolio level. Design and deliver custom analyses and models to answer ad‑hoc investment, portfolio management, and investor questions. Identify, define, and refine key investment metrics and analytical views that inform investment decisions and support positive business outcomes. Build and maintain Power BI dashboards that combine quantitative outputs and visual storytelling to support portfolio management and senior leadership. Partner with investment, finance, and investor relations teams to support due diligence, investor requests, marketing materials, and quarterly performance reporting. Engage users with analytically rigorous tools and datasets, creating a virtuous cycle where insights drive adoption and improved data quality. Configure eFront and assist users with troubleshooting and best practices. Contribute to the design and maintenance of AI‑ready datasets by optimizing and enhancing datasets for consumption by investment professionals. Clearly document analytical methodologies, assumptions, data standards, and repeatable processes.

Requirements

Bachelor’s and/or master’s degree in Finance, Economics, Statistics, Data Science, Computer Science, Financial Engineering, or a related quantitative field, or an equivalent combination of education and experience.
Two or more years of experience in a quantitative, analytics, investment, or performance measurement‑focused role.
Experience with investment performance measurement, return calculations, or portfolio analytics.
Strong data-driven decision-making mindset with comfort working in ambiguous, unstructured problem spaces.
Process-oriented approach with the ability to design scalable, repeatable analytical solutions.
Intellectual curiosity and willingness to challenge assumptions, methodologies, and existing analytical approaches.
High attention to detail and a strong instinct to reconcile, validate, and pressure-test data and results.
Strong proficiency in Python for data analysis, including pandas, numpy, and analytical workflows.
Experience building custom investment analyses, performance calculations, and repeatable analytical pipelines.
Proficiency in Power BI, including data modeling and integrating quantitative outputs into dashboards.
Advanced proficiency in Excel, including modeling and validation of analytical results.
Proficiency in Snowflake and dbt to query and build datasets.
Ability to interpret, scrub, process, and clearly explain complex quantitative results to non-technical stakeholders
Adaptive Communication: Formulates strategies to be used to convey complex information about services, products, systems, or processes to targeted audiences; communicates and liaises between technical and non-technical audiences.
Attention to Detail: Focuses on specific details to spot and correct errors in advance of them being found and surpass quality expectations. Performs work with thorough proofreading for presentation, content, accuracy, and overall quality.
Financial Market Monitoring: Consistently tracks and analyzes financial market data and news, evaluates trends and events that may impact investment portfolios or financial decisions, and makes informed decisions based on this information. Stays up to date on economic indicators, market news, and global events, and understands how they relate to investment opportunities and risks.
Industry Knowledge: Demonstrates extensive knowledge of common practices, regulatory considerations, market trends and the competitive landscape within a particular industry / sector to better understand and address unique client requirements, challenges, and context.
Risk Evaluation: Assesses the significance of identified risks, makes judgments about the significance of the risk in relation to the organization's objectives and assesses whether the risk is acceptable or not. Considers factors such as the organization's risk appetite, risk tolerance, and risk management resources to help decision-makers determine which risks require further action and which can be accepted or tolerated.

Nice To Haves

Experience with Snowflake, Python, dbt, and PowerBI preferred.

Responsibilities

Manage, transform, and integrate portfolio and benchmark data to support advanced quantitative analysis, ensuring information is accurate, auditable, and analytically robust.
Develop and maintain investment performance measurement frameworks, including returns, benchmarks, attribution, and performance drivers at the asset, strategy, and portfolio level.
Design and deliver custom analyses and models to answer ad‑hoc investment, portfolio management, and investor questions.
Identify, define, and refine key investment metrics and analytical views that inform investment decisions and support positive business outcomes.
Build and maintain Power BI dashboards that combine quantitative outputs and visual storytelling to support portfolio management and senior leadership.
Partner with investment, finance, and investor relations teams to support due diligence, investor requests, marketing materials, and quarterly performance reporting.
Engage users with analytically rigorous tools and datasets, creating a virtuous cycle where insights drive adoption and improved data quality.
Configure eFront and assist users with troubleshooting and best practices.
Contribute to the design and maintenance of AI‑ready datasets by optimizing and enhancing datasets for consumption by investment professionals.
Clearly document analytical methodologies, assumptions, data standards, and repeatable processes.