Business × Tech × AI

Xin Ni (Sydney) Jiang

Problem-solver at the intersection of business, technology, and AI — with a journey from banking to investment research to an AI startup.

New York, NY sydneyjiang00@gmail.com LinkedIn GitHub
No H-1B sponsorship required — STEM OPT through 2031, E-3 visa eligibility thereafter
Get in touch See my work ↓
0.82AUC — churn prediction model in production use
+22%campaign CTR from segmentation-driven ad strategy
30M+customer records modeled for credit default risk
3.91GPA — BA Economics, Boston University

01About

Quick to learn, endlessly curious, and biased toward action — I'd rather try something and adjust than just sit and wait. I work well with others because I actually enjoy it, and people can count on me to follow through.

My path has taken me from the trading floors of investment banking to the fast-moving world of an AI startup, and that range shaped how I think. I carry both classic business analytics fundamentals and a sharp instinct for where AI is headed — and how to actually put it to work to get more done, faster, without sacrificing quality.

I'll begin the MS in Business Analytics at Columbia University in Fall 2026, after finishing my BA in Economics and Statistics at Boston University.

Quick facts

  • Based inNew York, NY
  • Next upColumbia MSBA '28
  • FocusML · A/B testing · Risk
  • LanguagesEnglish · Mandarin
  • Work authNo H-1B sponsorship needed
    (STEM OPT through 2031)

02Skills

The toolkit behind the work.

Programming

Python (pandas · scikit-learn · NumPy)SQLRStata

Tools & Visualization

TableauPower BIAdvanced Excel (VBA · Pivot Tables)

Analytics & Modeling

Statistical ModelingMachine LearningA/B Testing NLP / Text AnalysisCausal InferenceCohort & Funnel Analysis Time-Series AnalysisBusiness Intelligence

Languages

Mandarin (native)English (fluent)

03Experience

Three industries, one thread: quantitative models that changed how a team made decisions.

ARISO

Analyst Intern
Remote / Santa Clara, CA · Sep 2025 – Present
  • Engineered a customer segmentation model in Python (pandas, scikit-learn) on 50K+ user interactions, surfacing 4 key personas that shaped targeted ad strategy and lifted campaign CTR by 22%+.
  • Built a churn prediction model (logistic regression + random forest) reaching AUC 0.82; the marketing team adopted its retention recommendations to prioritize high-risk segments.
  • Designed A/B tests across 3 landing-page variants, identifying the highest-converting registration funnel and driving an 18% lift in conversion rate.
  • Automated data pipelines connecting Google Ads and Search Console APIs to live Tableau dashboards — replacing 15 hours/week of manual reporting.
Pythonscikit-learnA/B TestingTableau +22% CTRAUC 0.82+18% conversion

CITIC Securities

Summer Intern
Dalian, China · Jul 2025 – Sep 2025
  • Conducted credit analysis on 10+ real-estate bond issuers using Bloomberg and Wind data, synthesizing macro and sector findings into reports that supported portfolio positioning under volatile policy conditions.
  • Built a multi-factor credit ranking model (DSCR, interest coverage, land-bank valuation), cutting per-issuer scoring time from 2–3 hours to under 1 hour.
  • Applied NLP sentiment analysis (LDA & BERT) to 500+ regulatory documents to quantify policy-risk exposure; findings entered senior PMs' bond-allocation framework.
Credit AnalysisNLP · LDA · BERTBloomberg / Wind 3× faster scoring

China Construction Bank

Credit Risk Analyst Intern
Dalian, China · Jun 2024 – Aug 2024
  • Built a credit default prediction model on 30M+ customer records, using stepwise feature selection to distill 1,104 variables down to the 105 most predictive attributes.
  • Tuned logistic regression hyperparameters to raise model AUC from 68% to 75%, enabling more reliable identification of high-risk borrowers before credit-line decisions.
  • Presented high-risk defaulter personas to the credit risk committee, directly informing credit-line adjustments across 50,000+ accounts.
Risk ModelingFeature SelectionLogistic Regression AUC 68→75%30M+ records

04Projects

Pick a record. Every sleeve is a real analysis — results and visuals right on the cover.

Project visualization
Now playing

05Research

Empirical economics research at Boston University — from global trade microdata to earnings-call text.

Firm Heterogeneity and Global Supply Chain Behavior in International Trade

Research Assistant · Supervisor: Prof. Stefania Garetto, Boston University · Mar 2025 – Sep 2025

Processed and standardized firm-level import and export transaction data across multiple countries and years, resolving inconsistencies in product codes, firm identifiers, and shipment classifications to prepare the dataset for empirical analysis.

Developed rule-based criteria to classify trade activities by firm type and transaction nature (e.g., intermediated vs. direct trade), enabling structured investigation into firm heterogeneity and global supply chain behavior; validated and documented data-cleaning decisions with the research team to build a clean, reliable panel dataset for studying international trade dynamics.

International TradeData CleaningPanel Data

Collusion in Plain Sight: Firms' Use of Public Announcements to Restrain Competition

Research Assistant · Supervisor: Prof. Juan Ortner, Boston University · Mar 2025 – Jul 2025

Analyzed earnings-call transcripts from major publicly traded firms to detect communication patterns indicative of tacit collusion in oligopolistic markets, contributing to an ongoing empirical study.

Designed an IO-theory-based classification framework and hand-labeled 1,000+ transcript excerpts, building a structured dataset that supports NLP and econometric analysis of firm behavior in concentrated industries.

Industrial OrganizationText ClassificationEconometrics

06Education

Columbia University

MS in Industrial Engineering — Business Analytics (MSBA)

New York, NY · Sep 2026 – Jan 2028 (expected)

Coursework: Optimization Analytics, Machine Learning, Advanced Visualizations, Gen AI & AI Strategies, Corporate Finance, Statistical Modeling, Data Science.

Boston University

BA in Economics · Minor in Statistics

Boston, MA · Sep 2023 – May 2026 · GPA 3.91/4.0

Foundation in econometrics, statistical inference, and economic theory — plus research assistantship in empirical industrial organization.

Let's talk data.

I'm open to analyst and data-science opportunities in New York and beyond. No H-1B sponsorship required — STEM OPT through 2031, with E-3 visa eligibility thereafter.

sydneyjiang00@gmail.com LinkedIn GitHub