Skills to Put on a Resume for an Applied AI Scientist

The skills real Applied AI Scientist postings screen for

Pulled from our Applied AI 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.

• Hugging Face Transformers

  Tool

  Hugging Face Transformers is a specific tool real postings for this role name by name — listing it precisely (not folded into "familiar with standard tools") is what gets it past an ATS keyword match.

  Evidence, not just a bullet: Point to one real thing you built or produced with Hugging Face Transformers, not just the tool name on a line by itself.

  Practice it:Build a 30-Day Readmission Risk Model on De-Identified EHR DataDrug-Repurposing Candidate Screen with Embedding Similarity

• RAG architectures

  Methodology

  RAG architectures is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied RAG architectures step by step, including what the output was.

  Practice it:Cost-Optimize an Embedding Pipeline for a Customer Support Knowledge Base

• 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:Forecast Intraday FX Volatility for a London Liquidity DeskCompare MDP Solvers for a Smart-Grid Battery Dispatch Pilot

• PyTorch or TensorFlow

  Naming a specific deep-learning framework — not just “machine learning” — says you’ve actually built and trained a model, not just called an API.

  Evidence, not just a bullet: Link a repo with a model you trained, even on a small dataset, and mention the architecture and one hyperparameter choice you made.

  Practice it:Build a Neural Surrogate for Computational Fluid Dynamics in HVAC DesignHardware-Aware NAS for a Wearable ECG Classifier

• 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:Measure HTTP/3 vs HTTP/2 Video Delivery Over CellularMine Basket History to Justify Grocery Shelf-Adjacency Changes

• Distributed training

  Technical

  Says you can train models across multiple GPUs or machines, not just on a single laptop — a scale problem distinct from the modeling itself.

  Evidence, not just a bullet: Reference a training run you scaled across devices and one bottleneck you had to work around (data loading, gradient sync).

  Practice it:Design a Distributed Training Job for a 13B-Parameter ModelAuto-Tune a Distributed Training Cluster's Throughput

• 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

• 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:Run a Human-Preference Study Comparing Two Coding Assistants

• Fine-tuning

  Technical

  Shows you can adapt an existing pretrained model to a specific task instead of training from scratch — the practical, resource-efficient skill most real ML work actually uses.

  Evidence, not just a bullet: Reference a model you fine-tuned, the base model you started from, and what changed in performance.

  Practice it:Build a Domain Instruction-Tuning Recipe for a Legal CoachFine-Tune a Vision-Language Model for Image Captioning

• 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

• Cross-functional collaboration

  Vague on its own — recruiters have seen it on nearly every resume — so it only lands when it's attached to a specific outcome across teams.

  Evidence, not just a bullet: Name the teams involved (“aligned Eng, Design, and Support”) and the decision or tradeoff you resolved between them.

  Practice it:Organizational Restructuring for a 40-Person SaaS Scale-upInvestigate How Circular-Fashion Ventures Build Their Launch Teams

• Kubernetes orchestration

  Tool

  One of the highest-demand infrastructure skills right now — proof you can run containerized workloads reliably at more-than-toy scale.

  Evidence, not just a bullet: Reference a specific manifest or Helm chart you wrote, and a real operational concern it handled (a liveness probe, an autoscaling rule, a rolling update).

  Practice it:Build a Multi-Tenant Kubernetes Foundation for Veterinary-Clinic SaaSCost-Optimize a Misshapen Kubernetes Cluster

• 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

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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  CodeIntermediateNew

  Forecast Intraday FX Volatility for a London Liquidity Desk

  You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …

  • Time Series Forecasting
  • Feature Engineering
  • Model Validation

  AI and Quantitative Finance
• 

  CodeIntermediateNew

  Build a Neural Surrogate for Computational Fluid Dynamics in HVAC Design

  Use a published CFD dataset (e.g., AirfRANS or a small in-house dataset if available) of around 1,000 steady-state airflow simulations on 2D building zones. Train a Fourier Neur…

  • Neural Operators
  • Surrogate Modeling
  • Computational Fluid Dynamics

  AI for Science and Engineering
• 

  CodeBeginnerNew

  Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot

  Model home-battery dispatch as a finite MDP: state is (state-of-charge, hour-of-day, current price tier), actions are charge/hold/discharge with realistic efficiency losses, tra…

  • Markov Decision Processes
  • Value Iteration
  • Policy Iteration

  Artificial Intelligence: Principles and Techniques
• 

  ResearchBeginnerNew

  Run a Human-Preference Study Comparing Two Coding Assistants

  Design a blinded paired-comparison study: 12 developer participants, each gets the same 8 realistic coding tasks (refactor, write a function, debug, test), each task is solved b…

  • Experiment Design
  • Statistical Evaluation
  • Human Evaluation

  AI Measurement and Evaluation
• 

  CodeIntermediateNew

  Forecast Energy Demand for a Nordic Renewable Utility

  You receive 5 years of hourly residential-segment demand, hourly weather data (temperature, wind, irradiance), and a calendar of public holidays. Build a probabilistic forecaste…

  • Time Series Forecasting
  • Probabilistic Modeling
  • Feature Engineering

  Applied Machine Learning
• 

  CodeIntermediateNew

  Plan Inventory Replenishment as an MDP for an E-Commerce AI Startup

  You receive 18 months of daily demand for 50 representative SKUs at one warehouse plus lead-time and unit-cost data. For one SKU at a time, formulate an MDP with state = (on-han…

  • Mdp Modeling
  • Value Iteration
  • Dynamic Programming

  Decision Making Under Uncertainty