Starbucks Store Portfolio Analysis (Tableau)
Built a decision-ready dashboard for store performance and market patterns, with clear KPI definitions and a tight “one-slide takeaway” for stakeholders.
open to internships · summer 2026
Modern, modest, data-forward
Tech Analyst Portfolio
I build analytics that helps teams make decisions—mixing SQL, Python, and dashboards with consulting-style storytelling. Strong at messy data → reproducible pipelines → crisp recommendations, with risk-aware validation and clear communication.
focus Analytics → risk / ops / product
stack Python · SQL · Tableau
style Exec-ready insights & storytelling
edge Systems thinking (process + data)
Selected Projects
case-study style · measurable · readable
Customer Spend Prediction (ML + Validation)
Built a regression pipeline with feature engineering, model comparison, and sanity checks. Focus: trustworthy validation and “what can go wrong” documentation.
Yelp + OpenStreetMap PostgreSQL (Schema + Pipeline)
Designed a relational schema, ingestion checks, and reproducible transforms for large public datasets. Outcome: fewer broken joins, faster iteration, cleaner handoffs.
Options Pricing Toolkit (Python)
Implemented and compared Black–Scholes, binomial trees, and Monte Carlo simulation. Emphasis: assumptions, sensitivities, and validation against baseline cases.
Market Structure Modeling (Renewables)
Modeled Cournot / Bertrand / Stackelberg competition to analyze pricing strategy trade-offs in energy markets, translating theory into actionable framing.
Case Notes
how I think
Problem → What decision is blocked? Who owns it? What metric moves?
Data → Source trust, freshness, missingness, definitions, and IDs.
Method → Simple first; add complexity only if it changes decisions.
Output → Dashboard + one-slide takeaway + risk register + next steps.
Impact Framing
measurable
• Reduced manual reporting via automation + guardrails
• Improved data reliability (fewer broken joins, cleaner keys, clearer definitions)
• Enabled faster decisions with consistent KPIs + exec-ready visuals
Experience
timeline · outcomes · ownership
Timeline
full time experience
Machine Learning Research Assistant — Obringer Lab
Jan 2025 — Jun 2025
ML modeling
Climate-energy data
Reproducible notebooks
- Built and evaluated models linking weather/climate features to renewable generation signals.
- Documented assumptions, limitations, and sensitivity checks for non-technical audiences.
- Delivered results as clear narratives + visuals (what changed, why it matters, what to do next).
Co-Founder & Quantitative Analyst — Enerquest Consulting LLC
Jun 2024 — Jun 2025
Client-ready outputs
Quant + story
Execution
- Produced decision-ready analysis (risk-aware framing, metrics discipline, and clear next steps).
- Built lightweight toolkits (SQL + Python) to speed up recurring work and reduce errors.
- Practiced “no fluff” communication: problem → analysis → decision → actions.
Business Technology Solutions Intern — Deloitte Consulting
Aug 2021 — Nov 2021
SAP
Process improvement
Cross-functional
- Supported SAP system updates and implementation risk assessment in a client environment.
- Coordinated across modules and stakeholders, translating requirements into clear deliverables.
- Helped standardize workflow inputs to reduce handoff friction and rework.
Signature Strengths
how teams use me
Clarify
scope + metric
Turn fuzzy asks into crisp questions. Define success, constraints, and “what decision changes” before touching the data.
1 North-star metric
2 Stakeholder map
3 Risk register
Build
clean + validate
Make the pipeline reliable. Add sanity checks so errors are visible and fixes are fast.
4 Data QA
5 Reproducible steps
6 Versioning
Decide
ship + explain
Deliver a decision-ready output: dashboard + one-slide takeaway + “what to do next” (including trade-offs).
7 Clear narrative
8 Impact estimate
9 Next actions
Signals I Care About
small details, big results
Definition discipline
Data integrity & IDs
Model sanity checks
Bias / leakage awareness
Slide-ready summarization
Stakeholder empathy
90-Day Roadmap (If You Hire Me)
practical · structured · creative
Days 0–14
learn fast
Get context, understand the data landscape, and ship something small but real to earn trust.
A stakeholder interviews
B metric definitions
C quick-win dashboard
Days 15–45
stabilize
Harden the pipeline, create QA checks, reduce manual work, and make reporting consistent.
D automated checks
E docs + handoffs
F single source of truth
Days 46–90
scale
Deliver an end-to-end case study: decision, impact, and what’s next. Build a reusable playbook.
G impact measurement
H dashboard v2
I playbook template
Hobbies: Chess, F1, Kayaking, Piano.
Skills
stack · analytics · communication
Technical
tools I ship with
Python (pandas, sklearn)
SQL (Postgres)
Tableau / BI
Git/GitHub
ETL & data modeling
GeoPandas / GIS
APIs & automation
SAP (FI/CO, PP, MM)
Analyst Toolkit
the “how”
Problem framing
KPI design
Experiment thinking
Model validation
Stakeholder comms
Deck writing
Risk-aware reasoning
Education
expected graduation date: 2026 Dec
Columbia University
M.S. in Applied Analytics
Aug 2025 — Dec 2026
Machine Learning
Data Engineering
Strategy & Analytics
Capstone
Pennsylvania State University
B.S. in Finance; Earth Sciences & Policy
Aug 2019 — May 2024
Statistics
Economics
Policy writing
GIS / spatial analysis
Awards: Dean’s List; Robert F. Schmalz Award in Geosciences.