You will get a ranking, recommendation, or ML experiment quality audit
Project details
I will audit your ranking, recommendation, search, or ML decision system and identify why model quality, ranking changes, or experiment results are not translating into better product metrics.
This is not a generic data analysis or model training service. I review the product algorithm pipeline behind the result: candidate generation, filtering, ranking/reranking, business rules, logging, metrics, offline evaluation, online experiment setup, and product impact.
I can help when recommendations feel irrelevant, search quality is hard to tune, a ranking model looks good offline but fails online, an A/B test is noisy or contradictory, or a metric improves while business impact remains unclear.
For deeper packages, I decompose product impact by segment, funnel stage, metric layer, and algorithm pipeline stage to identify where the strategy actually fails.
This is an audit, experiment analysis, and evaluation design service. Implementation, model training, ranking system changes, dashboards, or instrumentation can be scoped as follow-up work.
This is not a generic data analysis or model training service. I review the product algorithm pipeline behind the result: candidate generation, filtering, ranking/reranking, business rules, logging, metrics, offline evaluation, online experiment setup, and product impact.
I can help when recommendations feel irrelevant, search quality is hard to tune, a ranking model looks good offline but fails online, an A/B test is noisy or contradictory, or a metric improves while business impact remains unclear.
For deeper packages, I decompose product impact by segment, funnel stage, metric layer, and algorithm pipeline stage to identify where the strategy actually fails.
This is an audit, experiment analysis, and evaluation design service. Implementation, model training, ranking system changes, dashboards, or instrumentation can be scoped as follow-up work.
Machine Learning Tools
pandas, Python, PyTorch, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$49
|
Standard
$399
|
Advanced
$1,499
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 0 | 1 | 1 |
Number of Model Variations | 0 | 0 | 0 |
Number of Scenarios | 1 | 1 | 3 |
Number of Graphs/Charts | 0 | 0 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code | - | - | - |
Frequently asked questions
About Quinn
Senior AI/ML Engineer | 10+ Yrs | Ranking, Growth & LLM Reliability
Tokyo, Japan - 8:05 am local time
I’m a Senior AI/ML Engineer with 10+ years of experience building production machine learning, recommendation, user growth, and backend systems at leading internet technology companies. My work has supported large-scale consumer platforms, including global short-video and content ecosystems with hundreds of millions of daily active users.
What I can help with:
1. Product Algorithms & ML Systems
- Recommendation, ranking, matching, personalization, and search relevance
- Offline/online evaluation, model diagnosis, and A/B experiment design
- Predictive modeling, forecasting, classification, and decision systems
- Content intelligence, semantic analysis, and NLP pipelines
2. Growth Intelligence & Data-Driven SEO
- Query opportunity modeling and search traffic growth
- Programmatic SEO strategy for large-scale content/page systems
- Cohort analysis, uplift modeling, incentive allocation, and ROI optimization
- Data-driven growth experiments and performance measurement
3. Reliable LLM / RAG Systems
- RAG quality audits, retrieval improvement, and reranking strategy
- Hallucination reduction, source grounding, and citation-backed answers
- LLM evaluation, failure taxonomy, structured outputs, and test cases
- Production-readiness reviews for AI features and agentic workflows
I work best on problems where model quality, business metrics, and production constraints all matter: ranking quality, growth ROI, retrieval accuracy, evaluation design, latency, reliability, and maintainability.
If you need someone who can connect AI/ML capabilities with real product metrics, data pipelines, and production engineering, I can help diagnose, design, and ship a solution that works in practice.
Steps for completing your project
After purchasing the project, send requirements so Quinn can start the project.
Delivery time starts when Quinn receives requirements from you.
Quinn works on your project following the steps below.
Revisions may occur after the delivery date.
Review product goal and algorithm pipeline
I review your product goal, algorithm pipeline, ranking/search/recommendation setup, metric definitions, and available experiment context.
Inspect metrics, cases, and experiment evidence
I review the evidence you provide: dashboards, screenshots, experiment summaries, SQL/notebooks, sample cases, or exported tables.