Data & Risk Analyst

Transforming complex data
into decision-ready
dashboards & risk signals.

Experienced data & risk analyst with expertise in SQL, Python, BI solutions, credit risk & fraud analytics. Focused on building robust analytics systems that deliver ongoing results and support strategic decision-making.

SQL Tableau Python Automation Credit & Fraud Analytics
View Projects For Hiring Managers

Snapshot

  • Name: Rachit Shah
  • Location: Oregon, U.S. (Pacific Time)
  • Work auth: U.S. Permanent Resident (no sponsorship required)
  • Role preferences: Full-time · Remote-preferred · U.S.-based opportunities
  • Focus: Data / BI · Credit & Fraud Risk

Top 3 Projects

  1. Risk Analytics Flagship – Credit & Fraud Platform
  2. Contoso 100K – Sales & Customer Analytics
  3. Remote Data Analyst Job Market – Skills & Salary

About

Math + Economics + Automation + Modern Analytics.

I studied Mathematics at Oregon State University (with Economics and Actuarial Science minors), worked as an accounts manager in Mumbai, then moved into a remote data analyst internship at a U.S. startup after completing my undergraduate degree.

In 2025, I went all-in on upskilling: building a job-market analytics project, a 100K-row sales & cohorts project, and a full credit & fraud risk platform with Python, SQL, APIs, monitoring, and dashboards.

My sweet spot is roles where SQL, BI, and automation are central and where data decisions have real financial consequences – fintech, banking, SaaS, or any product that lives or dies on data.

  • Education: B.Sc. Mathematics (Oregon State), minors in Economics & Actuarial Science
  • Recent focus: Data / BI, credit risk, fraud analytics
  • Strengths: SQL, Python, Automation, risk pipelines, dashboards
  • Work style: Remote preferred, async-friendly, documentation-heavy

Skills

Tools & technologies I use to deliver results.

Data & BI Core

  • SQL (view, window functions, cohorts, churn/LTV)
  • Tableau (dashboards, KPIs, executive + deep-dive views)
  • Power BI (basic dashboards, DAX measures – expanding)
  • Exploratory data analysis and KPI design
  • Data storytelling and written reports

Risk & Fintech

  • Credit risk concepts (PD-style scoring, expected loss-style metrics)
  • Fraud analytics (features, rules engine, basic ML scoring)
  • Portfolio / cohort views for risk and revenue
  • Risk dashboards: delinquency, fraud rates, alert volumes

Data Engineering & Ops (Light)

  • Python (pandas, basic scikit-learn for classification)
  • FastAPI for scoring endpoints
  • MLflow, DVC, Evidently for tracking & monitoring
  • Windows Task Scheduler for daily chains
  • Basic cloud & dbt concepts (learning and applying via small projects)

Business & Finance

  • Accounting & reconciliations (former Accounts Manager)
  • Comfort with financial statements and basic ratios
  • Economics & probability / statistics foundations
  • Working knowledge of credit/fraud KPIs and trade-offs

Projects

Orchestrated the development of three key projects, providing a clear illustration of my work style and capabilities.

Risk Analytics Flagship – Credit & Fraud Platform

Python · SQL · FastAPI · MLflow · DVC · Evidently · Tableau

End-to-end risk system that simulates how a fintech manages credit and fraud: daily scoring, rules + ML, APIs, monitoring, and dashboards.

  • Credit track: PD-style scoring, expected-loss-style metrics, risk bands, and daily portfolio scoring chain.
  • Fraud track: feature pipelines, YAML-configured rules engine, ML scoring, FastAPI endpoint, and daily KPIs.
  • Governance: MLflow experiment tracking, DVC data snapshots, Evidently drift reports, and audit-pack scripts.
  • Operations: Windows Task Scheduler for daily jobs and a static “showroom” site plus local live demo.
View demo README View dashboard

Contoso 100K – Sales & Customer Analytics

SQL · Tableau / Excel · Cohorts · Retention · Revenue

Analysis of a 100K+ row transactional dataset to understand customer value, cohorts, churn, and revenue concentration – with clear business playbook.

  • Segmented customers into high/medium/low value and quantified contribution to total revenue.
  • Built cohort retention views to spot declining revenue per customer and early churn windows.
  • Created dashboards showing revenue over time by segment and cohort, with filters for quick deep dives.
  • Produced recommendations: defend top customers, grow mid-tier, reduce low-value drag.
README View dashboard

Remote Data Analyst Job Market – Skills & Salary

PostgreSQL · Tableau · Career Analytics

Analysis of data analyst job postings to understand how skills, salary, and remote options interact – used to design my own upskilling roadmap.

  • Used SQL to map skills (SQL, Python, Tableau/Power BI, cloud) to salary bands and remote status.
  • Identified “high-demand + high-salary” skill combinations for remote roles.
  • Built a dashboard for exploring salaries by location, experience, and skills.
  • Wrote a narrative report with concrete recommendations for aspiring remote analysts.
README View dashboard

For Employers

Key evaluation points for data analytics, risk, and business intelligence roles.

Evaluating Data & BI Skills

  • Review the Contoso 100K project to see SQL and dashboard work on a realistic transactional dataset.
  • Use the associated dashboards to assess KPI design, cohort and retention views, and how findings are communicated.
  • Look at the job market analysis to see how I combine SQL, BI, and written narrative.
  • In a live walkthrough, I can explain metric definitions, assumptions, and design trade-offs in detail.

Evaluating Credit & Fraud Risk Skills

  • Review the Risk Analytics Flagship repository and documentation.
  • Examine how credit scoring, expected-loss-style metrics, and portfolio monitoring are structured.
  • Evaluate the combination of rules and ML for fraud detection, along with API scoring and daily KPI outputs.
  • In discussion, we can map this architecture to your current data, tools, and risk appetite.

What I Bring to a Team

  • Ability to move from raw data to SQL models, reports, and dashboards.
  • Experience designing monitoring and basic governance around analytics workflows.
  • Clear written communication and habit of documenting decisions and assumptions.
  • Strong interest in fintech, credit, and fraud domains where data and risk are central.

For Startups & Challenge-Style Hiring

Portfolio-based and challenge-style evaluations are very welcome.

How I Work in Challenge Processes

  • You provide a representative dataset or problem statement aligned with the role.
  • I deliver a focused analysis, dashboard, or mini-pipeline within an agreed scope and timeline.
  • We review the solution together, focusing on reasoning, design choices, and potential next steps.

What You Can Assess Quickly

  • SQL and data modeling on your schema or a realistic sample.
  • Design of metrics and dashboards for your product or risk use case.
  • Approach to credit and fraud logic if you operate in financial services.

Why Portfolio-Based Hiring Fits

  • My strongest evidence is in shipped projects and repositories, not job titles alone.
  • I am comfortable discussing code, assumptions, and limitations openly.
  • This approach lets both sides quickly determine mutual fit based on real work.

Resume & Contact

Remote-preferred · Full-time roles based in the United States.

Resume & Profiles

Contact

  • Email: shahrachitn@gmail.com
  • Based in Oregon, U.S. (Pacific Time).
  • Open to full-time, remote-preferred roles based in the United States.
  • Available for introductory conversations and structured, role-relevant assessments.