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Data Science Proposal Template

Data science proposals win when they translate stats into dollars. Clients don't buy 'an XGBoost model' ā€” they buy 'reduced churn by 12%' or 'identified $400K in revenue leakage.' Lead with the business outcome and let the methodology be a footnote.

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What to include in your data science proposal template

  • Business question and success criteria (KPI-based)
  • Data inventory (sources, quality, access)
  • Methodology overview (without overwhelming jargon)
  • Iteration plan (baseline ā†’ improved versions)
  • Deliverables (model, code, dashboard, report)
  • Production deployment scope (or explicit non-inclusion)
  • Model handover and documentation
  • Ongoing monitoring and retraining (separate retainer)

How to price it

Data science projects: $15K-$50K (small targeted analysis), $50K-$200K (full ML pipeline build), $200K+ (enterprise with deployment). Avoid hourly billing ā€” clients underestimate the work and you'll fight every invoice.

Common mistakes to avoid

  • Promising specific accuracy numbers before seeing the data
  • Including production deployment without separate scoping
  • No 'data quality clause' (you can't model garbage)
  • Vague success criteria ā€” clients claim non-delivery
  • Missing IP terms (who owns the trained model?)

Sample template content

Here's an example of what a complete proposal looks like for this niche. Use it as a starting point ā€” you'll fill in your own details when you create one.

Scope of Work

Customer churn prediction model for [Client]: ā€¢ Discovery (week 1): stakeholder interviews, data audit, success metric definition ā€¢ EDA (week 2): exploratory analysis, feature ideation, baseline model ā€¢ Modeling (weeks 3-5): feature engineering, model selection, hyperparameter tuning ā€¢ Validation (week 6): out-of-time validation, business simulation, ROI estimate ā€¢ Handover (week 7): documentation, code review, deployment recommendations Deliverables: trained model artifacts, Python codebase, technical report, executive presentation

Sample Line Items

DescriptionQtyTotal
Discovery & EDA1$8,000.00
Model development1$22,000.00
Validation & business case1$7,000.00
Handover & documentation1$5,000.00
Total$42,000.00

Sample timeline: 7 weeks

Terms & Conditions

Fee: 30% on signing, 30% at end of EDA phase, 40% on final delivery. Client is responsible for data access and infrastructure. Delays in data delivery extend timeline 1:1. Client owns trained model and code upon final payment. Consultant retains right to use methodology in future engagements. No guarantee of specific accuracy ā€” model performance depends on data quality and signal availability.

Frequently asked questions

ā–ø Should I guarantee model accuracy?

Never before seeing the data. After EDA you can offer a target with caveats ('we'll aim for 80%+ AUC, with a fallback baseline guaranteed'). Pre-data guarantees blow up.

ā–ø Who owns the model?

Default: client owns the trained artifact and code. Consultant keeps the methodology and right to use approaches in other engagements. Document this.

ā–ø What about ongoing monitoring?

Separate engagement. Models drift. A monthly retainer for monitoring + quarterly retraining is the right structure.

Ready to send a winning data science proposal template?

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