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

About

I move fast, stay curious, and follow through, traits forged on the fast-paced trading floors and sharpened within an AI startup. That mix gave me classic business analytics fundamentals, a practical read on where AI is headed, and the instinct to put it to work without cutting corners.

Outside of work I keep building, academic models that can explore, tools that solve real problems. The AI world moves fast enough that there's no fixed playbook, so I learn as I go and use AI itself to go after problems nobody's solved yet.

Quick facts

  • Based inNew York, NY
  • Next upColumbia MSBA '28
  • FocusQuantitative Research · Risk Analytics · Applied AI
  • CredentialsCFA Level I Candidate
  • LanguagesEnglish · Mandarin
  • Work authNo H-1B sponsorship needed
    (STEM OPT through 2031)

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)

Skills

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