ANALYTICS PORTFOLIO JOB DESCRIPTION

Portfolio analytics stops making sense when the models behind it go untested and the business questions go unanswered. An Analytics Portfolio professional owns the translation between raw data and decisions that move revenue, retention, and investment allocation. The role spans customer lifecycle measurement, quantitative model development, and portfolio performance reporting depending on the organizational context. Seniority ranges from associate-level model builders to senior leads managing teams across regional and functional boundaries.

As the Analytics Portfolio professional, you will design and deliver analytical frameworks that connect portfolio performance data to business outcomes including churn prediction, revenue forecasting, and investment ROI. You will collaborate with cross-functional stakeholders across finance, product, customer success, and risk teams, operating within matrixed environments where prioritization and communication are as critical as technical execution.

Career Impact: Deep experience across customer lifecycle analytics, quantitative risk modeling, and portfolio performance measurement builds a profile that is sought across SaaS, FinTech, and asset management markets.

Business Impact: The analytical outputs this role produces directly inform retention strategy, credit policy decisions, and portfolio investment allocation, making it a high-visibility function for senior leadership.

Growth Opportunity: Progression from associate-level model development toward senior analytics leadership or quant finance specialization is a well-defined path, with demand growing across both enterprise and financial services sectors.

• Drive descriptive, predictive, and prescriptive analytics across portfolio performance domains to support retention, upsell, and investment ROI decisions.
• Develop and maintain analytical models that measure customer health, churn causality, and propensity to expand or contract.
• Deliver reporting on capacity plans, forecasts, actuals, and variance across multiple functions and regions to enable operational decision-making.
• Analyze portfolio composition and performance to surface risk-adjusted insights for portfolio managers and senior business leaders.
• Design and implement methodology for attributing performance changes to underlying risk factors, market conditions, or customer behavior.
• Monitor credit or customer portfolio performance against expectations and escalate material deviations through structured dashboards.
• Partner with data engineering, finance, and product teams to maintain performance data infrastructure and support ad-hoc analytical requests.
• Identify process and automation improvements that reduce turnaround time and increase analytical throughput for internal consumers.

• Bachelor's degree in Analytics, Finance, Mathematics, Statistics, Econometrics, or a related quantitative field, or equivalent work experience.
• Three or more years of analytics experience in a portfolio, credit risk, customer success, or investment management context, with demonstrated ability to translate data into business decisions.
• Proficiency in SQL and at least one scripting or statistical language for data manipulation, modeling, and analysis.
• Experience designing or interpreting A/B tests, propensity models, or forecasting frameworks in a business setting.
• Strong command of data visualization and reporting, with the ability to present complex findings clearly to non-technical stakeholders.
• Proven ability to manage multiple analytical workstreams independently under deadline pressure in a matrixed or cross-functional environment.

• Graduate degree in a quantitative discipline such as applied mathematics, quantitative finance, or econometrics.
• Experience in customer lifecycle analytics including churn modeling, lifetime value estimation, and cohort analysis within a SaaS or subscription business.
• Familiarity with stochastic modeling, ALM techniques, or fixed income analytics in an asset management or insurance context.
• Prior exposure to machine learning methods including classification, regression, or neural network applications in a production analytical environment.

• Customer Health Index or churn rate movement, reflecting model accuracy against actual retention outcomes.
• Forecast variance rate, measuring how closely revenue or portfolio projections track actual results.
• Dashboard adoption rate among business consumers, indicating analytical output utility and reach.
• Mean time to deliver ad-hoc analytical requests, measuring responsiveness to stakeholder needs.
• Model accuracy metrics such as precision and recall for propensity or escalation prediction models.
• Typical tools: Data querying (commonly SQL-based warehouses); visualization (commonly Tableau or PowerBI); scripting (commonly Python or R)

• Base Salary Range: $95,000 to $155,000 depending on seniority and industry segment.
• Bonus: 10 to 20 percent annual target, performance-based.
• Equity: Common in FinTech and SaaS contexts, less typical in development finance.
• Health Benefits: Medical, dental, and vision coverage standard across employers.
• PTO: 15 to 20 days annually, plus public holidays.
• Common Perks: Remote or hybrid flexibility, professional development budget, and conference access.

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Work authorization in the United States is required for all positions using this template. Employment is contingent on successful completion of a background check, which may include verification of education, prior employment, and where applicable, credit history relevant to financial roles. Reasonable accommodations will be provided to qualified individuals with disabilities throughout the application and employment process. All applicants are considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other characteristic protected under applicable federal, state, or local law.

The Analytics Portfolio leads descriptive, predictive, and prescriptive analytics across the customer success offerings portfolio, delivering insights on retention, upsell, and churn causalities. Reporting to Customer Success business leaders and partnering with analysts and data scientists in pricing, marketing, sales, and finance, this role shapes the roadmap for portfolio analytics and drives measurable MRR/ARR outcomes.

Key Responsibilities

• Drive descriptive, predictive and prescriptive analytics for the customer success offerings portfolio to understand, track and predict investment returns and deliver insights into portfolio effectiveness.
• Drive analytic models for targeted service offers to help Customer Success Managers and Account Managers improve the customer journey and positively impact MRR/ARR outcomes.
• Deliver insights and analytics for Customer Support, Case Volumes, Call Deflections, Case Escalation Prediction, Call Volume Forecasting, Routing Effectiveness, and Customer Survey metrics.
• Deliver insights and analytics for Professional Services, Partner Ecosystem, Services Delivery Utilization, Workforce Planning, Services Bookings, Backlog Forecasting, and Revenue Forecasting.
• Deliver comprehensive analytical and statistical reporting on capacity plans, forecasts, actuals, variance, and workforce trends across multiple functions and regions.
• Combine analytics, exploratory skills, and data intuition to deliver insights on Customer Adoption Index, Customer Health Index, churn causalities, and upsell opportunities.
• Determine opportunities for improvement across process, tooling, and data automation to optimize costs and decrease turnaround time for analytics consumers.
• Manage reporting and analytics resources and cultivate a winning culture measured by high retention and employee satisfaction of high-performing teams.

Required Qualifications

• Bachelor's or Master's degree in Analytics, Business Administration, Applied Mathematics, Statistics, Econometrics, or a closely related field.
• 14-15 years of experience in data and analytics with progressively increasing responsibility, including more than 3 years managing a team in a fast-paced hyper-growth environment.
• 4+ years of demonstrated experience solving complex business problems using advanced analytics and statistical techniques, with exposure to machine learning and predictive analytics.
• 3+ years of experience in Customer Success, Professional Services, or Customer Support with knowledge of customer lifecycle analytics including churn, upsell, cross-sell, and propensity analysis.
• Exposure to AI, Deep Learning, Neural Networks, and NLP.
• Exposure to big data platforms including Snowflake, Redshift, Azure, Matillion, and Hadoop.
• Experience with Enterprise Visualization Tools such as Tableau, QlikView, or PowerBI, and proficiency in MS Office Suite with advanced Excel skills.
• Ability to shape ambiguity, govern and prioritize in a matrixed environment, and synthesize complex results for senior leaders.

Embedded within IFC's Manufacturing, Agribusiness and Services Global Analytics unit based in Washington, D.C., the Analytics Portfolio extracts data from management information systems and prepares analytical materials supporting investment portfolio management and new business processing. Working closely with MAS management, sector and regional investment teams, and corporate departments including CPM and the Operations Management Unit, this role ensures data quality, knowledge retention, and actionable reporting that informs portfolio decisions across MAS.

Core Functions

• Extract data from management information systems and prepare materials for regular analyses and presentations to MAS management, including business reviews, quarterly portfolio reviews, and reports on profitability and new business pipeline.
• Support coordination with CPM and Operations Management Unit on corporate-wide initiatives including KPI tracking, portfolio evaluation, and impact exercises such as stress tests.
• Address ad-hoc information requests from MAS director, sector and regional teams, performing research and data analysis on MAS portfolio and new business pipeline.
• Provide knowledge management and application of best practices and IFC standards across MAS, acting as all-around support on portfolio information management and related policies.
• Develop and deliver training sessions related to MAS portfolio and new business pipeline data targeting portfolio and investment staff across MAS.
• Prepare and upload reports, tools, and resources to MASREPORTS SharePoint and maintain periodic data quality checks.
• Resolve data quality issues related to MAS portfolio and new business pipeline in IFC systems and provide analytical support to process improvements and related IT initiatives.

Qualifications and Experience

• Bachelor's degree in Finance, Accounting, Business, or equivalent.
• Minimum 3 years of relevant work experience.
• Excellent knowledge of Word, Excel, PowerPoint, OneDrive, and SharePoint, with aptitude to learn new systems and databases.
• Knowledge of IFC's portfolio procedures, databases, and systems such as IRP, iPortal, iDesk, EPMS, ODS, Business Intelligence Reporting, FSRS, Cognos, and Cubes is a plus.
• Excellent organizational, administrative, and time management skills with proven ability to work independently under pressure and meet tight deadlines.
• High degree of accuracy and attention to detail, with ability to think independently, analyze problems, and identify appropriate solutions.
• Excellent written and verbal communication skills in English.

Reporting to the Head of Credit Risk, the Senior Analytics Portfolio forecasts and monitors credit portfolio performance, with a focus on the no-fee overdraft product SpotMe. Partnering with data engineering, machine learning, and analytics teams, this role analyzes A/B test results and maintains performance data tables to improve credit models and policies that directly support loss management outcomes.

Primary Duties

• Forecast and monitor the portfolio performance of the no-fee overdraft product SpotMe and report on results by creating dashboards.
• Compare actual performance of SpotMe against expectations and report findings to relevant stakeholders.
• Work closely with data engineering, machine learning, and analytics teams to maintain credit portfolio performance-related data tables and Looker views.
• Answer ad-hoc requests from risk, finance, and product teams related to the performance of credit products.
• Analyze results of A/B tests and make recommendations on improving models and policies accordingly.

Skills and Qualifications

• Bachelor's degree in Engineering, Math, Finance, or another quantitative discipline, with a Master's degree in a quantitative focus as a plus.
• 4-7 years of analytical experience, preferably at a FinTech company.
• Excellent SQL programming and advanced Microsoft Excel skills.
• Experience designing A/B tests and analyzing results to recommend product or credit policy changes.
• Experience working in Looker or a similar business intelligence tool.
• Experience in Python, R, SAS, or a similar programming language.
• Detail-oriented with exceptional organizational skills and ability to work in a fast-paced environment and meet aggressive deadlines.
• Excellent written and oral communication skills.

Sitting at the intersection of quantitative finance and multi-asset portfolio strategy, the Associate Analytics Portfolio builds and deploys stochastic ALM models, optimization techniques, and risk analytics to support real-world investment decisions. Operating across PGIM, Insurance CIO, and ERM, this role develops tools for portfolio managers and risk managers while investigating the latest quantitative methods in ALM/LDI and machine learning to advance client portfolio outcomes.

Duties

• Help build, test, and deploy stochastic ALM models, optimization techniques, and risk analytics.
• Explain model results in terms of economic drivers, portfolio characteristics, and modeling assumptions.
• Develop and implement methodology for attributing changes in market value and other portfolio analytics to underlying risk factors.
• Develop tools for analyzing portfolio composition and performance to support portfolio managers.
• Develop tools for analyzing tail scenarios and other metrics to support risk managers.
• Design and optimize derivative-based hedging strategies and analyze the impact of different capital regimes on asset allocation strategies.
• Investigate applicability of the latest quantitative methods in ALM/LDI, machine learning, and other data science techniques to real-world investment strategy.
• Leverage existing models and quantitative capabilities across PGIM, Insurance CIO, and ERM.

Experience and Qualifications

• Master's degree in quantitative finance, math, physics, or comparable academic experience.
• Up to 5 years of experience in a quant role within banking, asset management, or insurance.
• Reliable knowledge of core quant finance topics including probability theory, statistics, stochastic models, options theory, yield curve construction, fixed income analytics, and CAPM.
• Experience coding asset classes including bonds, structured credit, equities, alternatives, derivatives, and liabilities such as life insurance and annuity contracts.
• Solid programming background including Python and some form of C, C++, or C#.
• Creativity, intellectual curiosity, and the ability to break down quantitative ideas for non-quant audiences.
• Entrepreneurial appetite and mindset for a growth business venture.