JPMorgan / Wealth Analytics
Portfolio analytics work sample for the Asset & Wealth Management team. AUM concentration by tier, risk-adjusted returns, advisor productivity scoring, and a churn risk model on synthetic client accounts hitting 87% classification accuracy on holdout.
RoleAnalyst, work sample
Duration2 weeks
Year2026
StatusShipped
The brief.
JPMorgan's Asset & Wealth Management group runs analytics across $4T+ in client assets. The team needs people who can connect portfolio-level metrics to advisor behavior and surface risk before it materializes. I built this as a recruiter-facing audition piece — the kind of analysis a Day 90 hire would actually deliver.
The work.
AUM_CONCENTRATION / Q1LIVE
UHNW retention
94.2%
↑ 2.1pp YoY
Sharpe (top 10%)
1.87
↑ 0.12 vs index
Churn risk flagged
$24.4M
12 accounts
How I approached it.
- Modeled 25 synthetic client portfolios across ultra-HNW, HNW, and mass-affluent tiers with realistic AUM distributions and asset class mixes.
- Built risk-adjusted return benchmarks using Sharpe and Sortino, with index comparisons by tier.
- Trained a churn risk classifier in scikit-learn (logistic regression baseline, random forest production) reaching 87% accuracy on holdout.
- Surfaced the strategic insight automatically: a 1% improvement in UHNW retention is worth ~$24.4M in preserved AUM at the modeled concentration.
The outcomes.
- Sent as a work sample with direct outreach to Gavin Peng (VP Analytics) and Kevin Bichoupan (ED, Data & Analytics).
- Demonstrated proficiency in financial domain modeling beyond pure BI delivery.
- Reusable as a template for any wealth/portfolio analytics interview.
The stack.
Power BI
Stakeholder-facing dashboard layer with DAX measures
Python
Data generation, churn model training, evaluation
scikit-learn
Logistic regression and random forest classifiers
pandas
Portfolio time series and tier aggregations