Data Science Teams | Experiment & Model Tracking
Data science teams need experiment tracking and flexible timelines. GitScrum supports time-boxed research, model versioning, and ML-to-engineering handoffs.
5 min read
Data science teams face unique challenges with iterative experiments, uncertain timelines, and research-heavy work. GitScrum adapts to these needs with flexible workflows, experiment tracking, and visibility into both research progress and production deployments.
Data Science Workflow
Work Categories
DATA SCIENCE TASK TYPES:
┌─────────────────────────────────────────────────────────────┐
│ │
│ RESEARCH (Exploratory): │
│ • Uncertain outcomes │
│ • Time-boxed, not estimate-driven │
│ • Success = learning, not just delivery │
│ Example: "Explore NLP approaches for sentiment (2 days)" │
│ │
│ EXPERIMENT (Hypothesis-driven): │
│ • Clear hypothesis to test │
│ • Defined success metrics │
│ • May succeed or fail (both valuable) │
│ Example: "Test BERT vs GPT for classification" │
│ │
│ DEVELOPMENT (Production): │
│ • Traditional development estimation │
│ • Build on validated experiments │
│ • Clear deliverables │
│ Example: "Implement recommendation API endpoint" │
│ │
│ MAINTENANCE (Operational): │
│ • Model monitoring and retraining │
│ • Data pipeline maintenance │
│ • Bug fixes and improvements │
│ Example: "Retrain fraud model with Q4 data" │
└─────────────────────────────────────────────────────────────┘
Experiment Tracking
EXPERIMENT BOARD:
┌─────────────────────────────────────────────────────────────┐
│ IDEATION │ ACTIVE │ ANALYSIS │ DECISION │
├──────────────┼─────────────┼────────────┼──────────────────┤
│ │ │ │ │
│ Clustering │ BERT vs GPT │ Feature │ → Productionize │
│ approaches │ comparison │ selection │ gradient boost │
│ │ │ results │ │
│ Graph-based │ Gradient │ │ → Abandon │
│ recommender │ boosting │ │ RNN approach │
│ │ optimization│ │ │
│ Real-time │ │ │ → More research │
│ anomaly │ │ │ graph approach │
│ detection │ │ │ │
│ │ │ │ │
└──────────────┴─────────────┴────────────┴──────────────────┘
Adapting Agile
Sprint Planning
DATA SCIENCE SPRINT STRUCTURE:
┌─────────────────────────────────────────────────────────────┐
│ 2-WEEK SPRINT │
├─────────────────────────────────────────────────────────────┤
│ │
│ ALLOCATION GUIDELINES: │
│ • 60% Committed work (production, maintenance) │
│ • 30% Experiments (time-boxed research) │
│ • 10% Learning (papers, tools, upskilling) │
│ │
│ SPRINT EXAMPLE: │
│ │
│ COMMITTED (60%): │
│ • Deploy recommendation model v2.3 │
│ • Fix data pipeline timeout issue │
│ • Document model training process │
│ │
│ EXPERIMENTS (30%): │
│ • Compare BERT vs GPT-2 for classification (3 days) │
│ Success: Determine which performs better │
│ • Explore graph features for fraud detection (2 days) │
│ Success: Identify promising signals │
│ │
│ LEARNING (10%): │
│ • Review recent papers on transformer efficiency │
│ • Explore new MLOps tooling │
└─────────────────────────────────────────────────────────────┘
Estimation Approach
ESTIMATION BY WORK TYPE:
┌─────────────────────────────────────────────────────────────┐
│ │
│ RESEARCH/EXPERIMENTS: │
│ Use TIME-BOXING: │
│ "Spend 2 days exploring this. Report findings." │
│ NOT: "Estimate how long to find a solution." │
│ │
│ Typical time boxes: │
│ • Quick spike: 4 hours │
│ • Standard experiment: 2-3 days │
│ • Deep research: 1 week │
│ │
│ PRODUCTION DEVELOPMENT: │
│ Use STORY POINTS: │
│ • Clear requirements │
│ • Known technology │
│ • Comparable to past work │
│ │
│ HANDLING UNCERTAINTY: │
│ Phase 1: Explore (time-boxed) → Learning │
│ Phase 2: Prototype (rough estimate) → Working code │
│ Phase 3: Productionize (firm estimate) → Deployed │
└─────────────────────────────────────────────────────────────┘
Model Development Workflow
Model Lifecycle
MODEL DEVELOPMENT STAGES:
┌─────────────────────────────────────────────────────────────┐
│ │
│ PROBLEM DEFINITION │
│ │ • Business problem clear │
│ │ • Success metrics defined │
│ │ • Data availability confirmed │
│ ▼ │
│ DATA EXPLORATION │
│ │ • Understand data quality │
│ │ • Identify features │
│ │ • Baseline established │
│ ▼ │
│ MODEL EXPERIMENTATION │
│ │ • Try multiple approaches │
│ │ • Track experiments systematically │
│ │ • Select best performer │
│ ▼ │
│ MODEL DEVELOPMENT │
│ │ • Production-ready code │
│ │ • Testing and validation │
│ │ • Documentation │
│ ▼ │
│ DEPLOYMENT │
│ │ • API/batch integration │
│ │ • Monitoring setup │
│ │ • A/B testing if applicable │
│ ▼ │
│ MONITORING & ITERATION │
│ • Track model performance │
│ • Detect drift │
│ • Plan retraining │
└─────────────────────────────────────────────────────────────┘
Team Collaboration
DATA SCIENCE + ENGINEERING HANDOFF:
┌─────────────────────────────────────────────────────────────┐
│ │
│ DATA SCIENCE DELIVERS: │
│ ✓ Trained model artifact │
│ ✓ Model card (performance, limitations) │
│ ✓ Feature requirements │
│ ✓ Expected input/output formats │
│ ✓ Performance benchmarks │
│ │
│ ENGINEERING PROVIDES: │
│ ✓ Feature pipeline infrastructure │
│ ✓ Model serving platform │
│ ✓ Monitoring and alerting │
│ ✓ A/B testing framework │
│ ✓ Scaling and reliability │
│ │
│ SHARED RESPONSIBILITIES: │
│ • Integration testing │
│ • Performance optimization │
│ • Incident response │
│ • Documentation │
└─────────────────────────────────────────────────────────────┘