Present a DS project with business impact

# How to Structure and Deliver a Strong 7-Minute DS Project Talk Below is a step-by-step framework, a slide-by-slide timing plan, and a complete example you can adapt. The example uses a consumer-services marketplace scenario that maps well to many product/data contexts. ## Time & Slide Plan (7 minutes + 60-second roadmap) - Slide 1 (1:00): Problem, decision, north-star metric - Slide 2 (1:15): Data sources, assumptions, risks - Slide 3 (2:00): Modeling/analysis approach and rejected alternatives - Slide 4 (1:30): Results with confidence intervals and sensitivity checks - Slide 5 (0:45): Shipped impact vs. projected; post-launch validation - Slide 6 (0:30): Two failures/trade-offs - Roadmap (60 seconds): Next iteration plan ## Example Presentation You Can Deliver Title: Improving Booking Conversion with a Lead-Quality Model 1) Business Problem, Decision, North-Star Metric - Context: In a services marketplace, customers submit job requests (e.g., plumbing), and professionals (“pros”) respond. Low-quality leads reduce match rates, increase refunds, and hurt pro retention. - Decision at stake: Replace a rule-based lead distribution with a machine-learning lead-quality score to prioritize which requests to surface, notify, and incentivize. - North-star metric: Net Jobs Booked (booked jobs minus refunded/canceled) within 14 days of request. Secondary metrics: refund rate, pro response rate, and gross booking value per request (GBV/R). 2) Data Sources, Key Assumptions, Risks - Data sources: - Historical requests: category, location, time, budget, textual description length. - Buyer signals: prior request history, device, on-site behaviors (e.g., message opens). - Pro supply signals: nearby supply density, pro ratings, response latency. - Outcomes: whether the request led to a booked job within 14 days; refunds. - Labels: y = 1 if booked within 14 days; else y = 0. - Key assumptions: - 14-day window captures >95% of bookings and is stable across categories. - Historical outcomes are representative of future behavior (stationarity). - Logged features are complete and timestamped to avoid label leakage. - Risks and mitigations: - Selection bias: Only leads exposed to pros can become bookings. Mitigate via inverse propensity weighting in offline evaluation and by running an online A/B test. - Leakage: Post-request signals (e.g., quote count) could leak future information. Strict feature windowing and feature audits prevent leakage. - Drift/seasonality: Add time features, monitor calibration and AUC weekly, retrain monthly. - Cold starts: Back-off to category-geo priors when features are sparse. 3) Modeling/Analysis Approach and Alternatives Rejected - Problem framing: Binary classification to estimate P(booking | request, context). We use the score to rank and set policy thresholds (e.g., notify top X%). - Approach: - Baseline: Heuristic rules from domain knowledge (e.g., minimum budget, category filters). Baseline AUC ≈ 0.62. - Model: Gradient Boosted Trees (XGBoost) for non-linearities and feature interactions; 5-fold time-split cross-validation. - Calibration: Isotonic regression so scores map to well-calibrated probabilities used for policy tuning and LTV simulations. - Offline metrics: AUC, PR-AUC, Brier score (calibration), and top-decile lift. - Policy simulation: Convert calibrated probabilities into expected bookings under different thresholds; choose threshold to maximize expected Net Jobs Booked subject to guardrails (refund rate non-increasing). - Why not these alternatives (and why): - Deep learning (tabular MLP): Rejected for interpretability, marginal lift in early tests, higher latency, and infra complexity. - Uplift modeling: Requires randomized notifications/exposure at scale and more complex experimentation; targeted for v2. - Two-sided optimization (supply constraints) end-to-end: Scoped out for v1 to reduce coupling; we used a modular ranking + simple throttling policy first. 4) Results with Confidence Intervals and Sensitivity Checks - Offline: - AUC: 0.79 (model) vs. 0.62 (baseline). - Top 10% leads by score captured ~3.1× booking rate of average traffic. - Policy simulation projected +3.5% to +5.0% Net Jobs Booked at steady state. - Online A/B Test (50/50, 2 weeks; clusters by city to limit interference): - Control booking rate: p_c = 11.8% (n_c = 120,000 requests) - Treatment booking rate: p_t = 12.3% (n_t = 118,000 requests) - Absolute uplift: Δ = p_t − p_c = 0.5 pp (relative +4.2%) - 95% CI for Δ using normal approximation: - SE = sqrt[p_c(1−p_c)/n_c + p_t(1−p_t)/n_t] - Numerically: SE ≈ sqrt(0.118×0.882/120,000 + 0.123×0.877/118,000) ≈ 0.001335 - CI = 0.005 ± 1.96 × 0.001335 ≈ [0.0024, 0.0076] (i.e., +0.24 to +0.76 pp) - Refund rate: 3.1% → 2.9% (Δ = −0.2 pp; 95% CI ≈ [−0.35, −0.05] pp) - Secondary guardrails: Pro response rate flat; latency +5 ms (within SLO). - Sensitivity Checks: - Label window: 7/14/21-day windows produced consistent ranking (Spearman 0.96+); v1 stuck with 14-day for stability. - Segment robustness: Gains observed across top categories and geos; no single segment dominated uplift. - Calibration: Reliability plots within ±2% in mid-probability bins after isotonic calibration. - CUPED-adjusted analysis reduced variance; conclusions unchanged. 5) Shipped Impact vs. Projected Impact; Post-Launch Validation - Projected (offline sim): +3.5% to +5.0% Net Jobs Booked. - Shipped (ramped to 100%): +3.8% (95% CI: +1.6% to +6.0%). Slightly below mid-point of projection due to tighter notification throttling after week 1 (ops feedback on message volume). - Post-launch validation: - Kept a 5% long-lived holdout for 4 weeks; effects persisted. - Difference-in-differences across cities to sanity-check seasonal drift. - Monitoring: Weekly drift checks (PSI on key features), AUC/calibration tracking, and alerting on refund rate. 6) Two Failures/Trade-offs We Consciously Accepted - Coverage trade-off: Limited v1 to top 12 categories (~65% of volume) to ensure model stability; long-tail users saw no improvement in v1. - Objective short-termism: Optimized bookings within 14 days, not LTV. This may underweight high-value but slower-to-close categories. 60-Second Roadmap (Next Iteration) - Expand scope and objectives: - Incorporate LTV-weighted targets and category-specific calibration. - Roll out to long-tail categories with transfer learning and hierarchical priors. - Smarter decisioning: - Move from probability model → causal uplift + contextual bandits for notifications. - Dynamic thresholds by segment (geo, category, supply density). - Reliability & fairness: - Real-time features (e.g., live supply load), quarterly bias audits, and automated regression tests for policy changes. ## Teaching Notes: How to Adapt This to Your Project - Replace the marketplace context with your domain, but keep the structure: decision → metric → data/assumptions → method → results with CIs → shipped impact → trade-offs → roadmap. - If you lack an online experiment: - Use quasi-experimental designs (matched controls, diff-in-diff), show sensitivity to unobserved confounding (e.g., Rosenbaum bounds), and report uncertainty. - Confidence intervals refresher (difference in proportions): - CI(Δ) = (p_t − p_c) ± z_{1−α/2} × sqrt[p_t(1−p_t)/n_t + p_c(1−p_c)/n_c] - For small samples or clustering, prefer bootstrap or cluster-robust variance. - Common pitfalls: - Label leakage, selection bias from policy exposure, non-stationarity, and misaligned metrics (optimize for proxy not business outcome). - Guardrails to mention in interviews: - Predefine metrics and stopping rules; calibrate and segment-check; keep a holdout; monitor drift; and define safe rollback criteria.