Skills to Put on a Resume for a Data Scientist

The skills real Data Scientist postings screen for

Pulled from our Data Scientist role taxonomy — not a generic list. Each one names what a recruiter reads into it and, more usefully, how to actually back it up.

• Causal inference

  Methodology

  Distinct from correlation-spotting — this says you can reason about, and test for, whether X actually caused Y, using methods built for that question.

  Evidence, not just a bullet: Reference one analysis where you addressed a confound or used a method (diff-in-diff, an instrumental variable, a controlled experiment) built to isolate causation.

  Practice it:Design a Causal Measurement Plan for EcoWear's Ad Campaign

• Data Visualization

  Soft skill

  The skill that turns analysis into something a non-technical stakeholder actually acts on — chart choice, not just chart-making.

  Evidence, not just a bullet: Link a dashboard or a single chart you built and explain the decision it drove, not just the tool (Tableau, Power BI, matplotlib) you used.

  Practice it:Build a Public Open-Data Dashboard for Urban MobilityExperimental Design for a Fintech App's Savings Nudge

• SQL

  Technical

  SQL is one of the most-requested technical skills on data and analyst job posts — it signals you can pull your own numbers instead of waiting on someone else.

  Evidence, not just a bullet: Show 2-3 real queries you've written — a join, a GROUP BY with aggregation, ideally a subquery or window function — not just “SELECT * FROM table”.

  Practice it:Redesign a Vet-Clinic SaaS Database to Third Normal FormAudit Data Quality for a Climate Tech Sensor Network

• Python or JavaScript

  Listing a language only matters if you can point to something it built — recruiters skim past “Python” unless there's a repo or project attached.

  Evidence, not just a bullet: Link a GitHub repo with a script that solves a real problem — data cleaning, an API integration, a small app — with a README that explains what it does.

  Practice it:Build a Bayesian Credit-Scoring Model for an Emerging-Markets FintechBuild a Credit-Card Fraud Detector for a Singapore Neobank

• Docker

  Tool

  Shows you can ship something that runs the same on your laptop as it does in production — a basic but non-negotiable expectation now.

  Evidence, not just a bullet: Link a repo with a Dockerfile you wrote and explain one non-obvious choice in it (a multi-stage build, a specific base image, a health check).

  Practice it:Containerize a Legacy Java Monolith and Move It to AWS ECSMigrating a 40-Person SaaS Scale-Up to Cloud-Native Architecture

• Apache Spark

  Signals you can process data at a scale that doesn't fit in a single machine's memory — a different skill from pandas-scale analysis.

  Evidence, not just a bullet: Reference a Spark job you wrote and the dataset size or transformation that actually needed distributed processing.

  Practice it:Migrate a 200TB Data Lake from Parquet to IcebergHalve a Daily Spark Bill Without Breaking the SLA

• AWS or Azure

  Cloud fluency recruiters filter on almost by keyword-match — but “used AWS” and “architected on AWS” read very differently.

  Evidence, not just a bullet: Name the specific services you’ve actually configured — e.g. set up an S3 lifecycle policy, wrote a Lambda, configured an IAM role — not just used passively.

  Practice it:Implement Authenticated Encryption for a Document ServiceContainerize a Legacy Java Monolith and Move It to AWS ECS

• A/B Testing

  Methodology

  A/B testing is proof you think in experiments, not opinions — you can design a test, pick a metric, and read a result without over-claiming significance.

  Evidence, not just a bullet: Describe one test end to end: the hypothesis, the metric that moved (or didn't), and what you'd do next.

  Practice it:MCMC for Conversion-Funnel A/B Testing at a Marketplace

• Experimental design

  Methodology

  Distinct from just running an A/B test — this is the skill of designing the experiment correctly in the first place (control groups, randomization, confounds) before any data comes in.

  Evidence, not just a bullet: Describe one experiment you designed from scratch, including a confound you controlled for or a randomization choice you made.

  Practice it:Experimental Design for a Fintech App's Savings Nudge

• Feature engineering

  Technical

  Feature engineering is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "Feature engineering" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Stack Five Models for a Kaggle-Style Forecasting Bake-OffAnalyze a Learning-Analytics Dataset for At-Risk Detection

• Git

  Tool

  Not really a differentiator on its own anymore, but a messy Git history (force-pushes over main, no commit messages) is a real red flag recruiters notice.

  Evidence, not just a bullet: Point to a repo with a clean, readable commit history — that's the evidence, more than the word “Git” on a line by itself.

  Practice it:Branch Predictor Study for a Cryptocurrency Hashing Library

Every challenge below is an AI-generated practice brief — not a real client engagement — that produces a submission you can point to as evidence for the skills above.

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  AnalysisIntermediateNew

  Build a Bayesian Credit-Scoring Model for an Emerging-Markets Fintech

  You receive an anonymized snapshot of about 30,000 historical applications with features (income proxy, tenure on platform, prior loans, region) and the binary default outcome. …

  • Bayesian Learning
  • Credit Scoring
  • Model Evaluation

  Advanced Machine Learning
• 

  CodeBeginnerNew

  Stack Five Models for a Kaggle-Style Forecasting Bake-Off

  You receive a pseudonymized dataset of 24 months of daily shipment volumes across about 200 origin-destination lanes plus weather and holiday features. Train 5 base models, use …

  • Ensemble Methods
  • Time Series Forecasting
  • Feature Engineering

  Advanced Machine Learning
• 

  AnalysisBeginnerNew

  Analyze a Learning-Analytics Dataset for At-Risk Detection

  You receive an anonymized dataset of LMS engagement features (logins, assignment submissions, forum posts, video-watch time), grade history, and a binary label for end-of-semest…

  • Learning Analytics
  • Classification
  • Fairness Metrics

  AI in Education and Learning Analytics
• 

  CodeBeginnerNew

  Build a Credit-Card Fraud Detector for a Singapore Neobank

  You receive 9 months of anonymized authorization data (around 8 million transactions, around 0.4 percent fraud) plus current rule outcomes. Split temporally and train at least t…

  • Classification Modeling
  • Class Imbalance
  • Model Calibration

  AI and Quantitative Finance
• 

  CodeBeginnerNew

  Predict Catalyst Properties for a Green-Hydrogen Pharma Spinout

  Use an open catalyst dataset (e.g., Open Catalyst Project subset, or a Materials Project pull) where each candidate has descriptors and a target activity property. Train a tabul…

  • Tabular Modeling
  • Uncertainty Quantification
  • Feature Engineering

  AI for Science and Engineering
• 

  CodeBeginnerNew

  Build a Fairness Evaluation Harness for a Credit-Score Model

  Implement a Python module that, given model predictions, ground truth, and group identifiers, computes demographic parity difference, equal-opportunity difference, predictive-pa…

  • Algorithmic Fairness
  • Statistical Evaluation
  • Python

  AI Measurement and Evaluation