Build a Predictive Talent Model from LMS Data in 90 Days

How to Build a Predictive Talent Model Using LMS Data in 90 Days

Building a predictive talent model from learning management system feeds is a practical route to faster, data-driven talent decisions. In our experience, a focused 90-day plan can move an organization from exploratory analytics to a validated scoring system that drives promotions, learning nudges, and talent pools. This article lays out a week-by-week project plan, data schema and features, team roles, validation checklists, rollout A/B tests, and a templated project charter you can start with today.

90-Day Week-by-Week Project Plan

Week 1–2: Discovery — Stakeholder interviews, KPI alignment (promotion lift, retention, time-to-productivity). Determine acceptance criteria and success metrics for the predictive talent model.

Week 3–4: Data pipeline MVP — Ingest LMS logs, course metadata, assessment results, and HR master data. Build an annotated ETL flowchart and run initial data quality checks.

Weeks 5–8: Feature engineering and baseline model

Feature engineering focuses on completion rate, time-to-complete, assessment gap, reattempts, and social learning signals. Train a baseline model (logistic regression or tree ensemble) and generate talent scoring. We've found starting simple yields fast, interpretable wins.

Weeks 9–12: Model validation, stakeholder demos, and production readiness

Run holdout validation, calibration, and bias audits. Prepare stakeholder demos that show ROC curves, precision at top-K, and the expected operational impact of talent scoring. Finalize deployment automation and monitoring.

• Deliverables: ETL flowchart, feature catalog, baseline model, validation report, demo slides.
• Success criteria: AUC > target (e.g., 0.70), precision@10% uplift vs. random, data freshness <24h.

Team Roles, Time Estimates, and Governance

Successful projects need the right mix of skills and time commitment. Below are recommended roles and estimated allocations for a 90-day build.

• Project Manager (0.3 FTE): Roadmap, stakeholder syncs, risk management.
• Data Engineer (0.8 FTE): ETL, data quality, pipeline automation.
• ML Engineer / Data Scientist (1.0 FTE): Feature engineering, modeling, validation.
• HR/People Ops SME (0.2 FTE): Domain mapping, interpretation, ethical review.
• Product/Business Owner (0.2 FTE): Acceptance criteria, rollout decisions.

Governance: establish a weekly review cadence, a change-control policy for model updates, and a documented bias mitigation checklist. A pattern we've noticed: projects that allocate clear ownership for data lineage and change control reduce surprises at deployment time.

Data Schema, ETL Flow, and Feature List

Design the LMS data modeling stage to capture events at the atomic level. Below is a compact sample schema and recommended features that power a robust predictive talent model.

Table	Key Fields	Notes
lms_events	user_id, event_type, timestamp, course_id, duration_seconds, score	Raw activity; ingest as event stream.
courses	course_id, competency_tags, difficulty, version	Enrich events with competency context.
hr_master	user_id, hire_date, role, manager_id, location	Essential demographic features for modeling.

Core feature list (engineer-friendly):

1. completion_rate = completed_courses / assigned_courses (30d, 90d windows)
2. time_to_complete = median time from assignment to completion
3. assessment_gap = expected_score - actual_score per competency
4. reattempts = count of retakes per assessment
5. social_learning_signals = posts, replies, peer endorsements
6. learning_velocity = new competencies acquired per quarter

ETL annotations: transform timestamps to rolling windows, compute cohort baselines, and join HR master with deterministic keys. If LMS taxonomy is rigid, consider mapping multiple course tags into a skill ontology for better talent scoring.

Model Prototyping, Validation Checklist, and Acceptance Criteria

Model prototyping is iterative. Start with interpretable models, then graduate to ensembles. Our standard validation checklist ensures the predictive talent model is robust, fair, and deployable.

What should the validation checklist include?

• Data quality: missingness <5% for key fields, no drift in timestamps.
• Performance: AUC, precision@K, recall, calibration plots.
• Fairness: group parity tests and disparate impact thresholds.
• Stability: time-split validation and feature importance stability.
• Operational: scoring latency, monitoring hooks, rollback plan.

Acceptance criteria example:

• AUC ≥ 0.70 and precision at top 10% ≥ baseline + 15%.
• Calibration error within acceptable bounds (Brier score threshold).
• No protected group shows >20% disparate impact without a remediation plan.

We've found that explicit calibration and top-K precision metrics are more actionable for HR than aggregate accuracy alone.

Rollout Strategy and A/B Test Plan for Promotions or Talent Pools

Rollouts should be conservative and measurable. Use a staged A/B test to compare traditional selection vs. model-driven selection for promotions or talent pool inclusion.

How to structure the A/B test?

1. Define the treatment: top-K by predictive talent model score invited to accelerated promotion path or targeted development.
2. Randomize at the manager or team level to prevent selection contamination.
3. Primary metrics: promotion rate lift, 6–12 month performance delta, retention improvement.
4. Secondary metrics: manager satisfaction, candidate experience scores, cost per promoted role.

Example rollout phases: pilot (5% population), expand (25%), enterprise (full). Monitor model drift, feedback loops from managers, and operational KPIs. While traditional systems require constant manual setup for learning paths, some modern tools (like Upscend) are built with dynamic, role-based sequencing in mind, which reduces maintenance overhead and improves the signal quality feeding your model.

Sample Notebook Pseudocode and Project Charter Template

Below is compact pseudocode you can adapt to prototype quickly and a templated project charter to align stakeholders.

# Pseudocode (notebook-style) 1. load(lms_events, courses, hr_master) 2. feature_df = aggregate_features(lms_events, windows=[30,90,180]) 3. feature_df = join(feature_df, hr_master) 4. train, test = time_split(feature_df, cutoff='2024-01-01') 5. model = train_model(train, algo='xgboost', eval_metric='auc') 6. eval = evaluate(model, test, metrics=['auc','precision_at_k','calibration']) 7. save_model_and_metrics(model, eval)

Project Charter (templated)

Field	Details
Project Name	90-day Predictive Talent Model from LMS Data
Objective	Deliver a validated talent scoring system to support promotions and development decisions with measurable lift vs. current practice.
Scope	Ingest LMS + HR master, build features, prototype model, validate, pilot rollout.
Success Metrics	AUC ≥ 0.70; precision@10% uplift ≥15%; deployment pipeline with daily scoring.
Risks & Mitigations	Data quality delays (mitigate with sample-based QA); executive buy-in (mitigate via pilot ROI deck).
Owner	People Analytics Lead

Common pain points and remedies: limited data quality can be triaged with a prioritized QA checklist and synthetic feature imputation; executive buy-in is best gained through a tight pilot and ROI projection; change control requires a documented rollback plan and model governance board.

Conclusion & Next Steps

Building a predictive talent model using LMS data in 90 days is achievable with focused scope, a small cross-functional team, and clear acceptance criteria. Start with lightweight ETL and interpretable models, prove impact with a pilot A/B test, then scale into production with monitoring and governance.

Key takeaways: prioritize high-signal features (completion_rate, assessment_gap, social_learning_signals), enforce data quality gates, and use measurable success criteria tied to HR outcomes. In our experience, teams that combine fast prototyping with disciplined validation get stakeholder buy-in and operational value quickly.

Next step: Use the project charter above to set a 30-day pilot and schedule the initial stakeholder demo in week 8. If you want, export the pseudocode into a shared notebook and run the feature aggregation on a one-week sample to prove feasibility.

Call to action: Begin by scheduling a 60-minute discovery session with your HR and data teams to finalize KPIs and the sample dataset for the pilot.