Vetted A/B Testing Professionals

“Performance marketer specializing in high-spend search and multi-platform paid media (Google Ads, Bing, Meta, TikTok), with a standout case doubling WinkBeds monthly revenue from $1M to $2M while materially improving ROAS on both Google and Bing. Strong in rigorous experimentation (Google Ads Experiments), campaign consolidation to eliminate overlap, and tracking/brand protection—successfully identifying and stopping affiliate ad hijacking that caused a conversion drop.”

“AI-focused product engineer working on LLM routing, prompt engineering, and multimodal API integrations at production scale. They describe improving system accuracy, latency, and token usage, fine-tuning an internal model to reduce third-party API dependence, and adding safety guardrails through prompt-injection testing and red-team evaluation.”

“Full-stack product engineer who owned a live-events digital ticketing platform end-to-end, including blockchain-based ticket validation and high-traffic booking flows. Stands out for combining Angular/React frontend work with Java/Spring Boot backend architecture, plus strong production reliability practices around concurrency control, queues, observability, and UX optimization.”

Bradley Krupkin

Screened ReferencesStrong rec.

Executive Digital Marketing Leader specializing in performance media, SEO, and e-commerce growth

Troy, MI15y exp

“Paid media strategist with ~25 years of client-facing performance marketing experience, owning seven-figure monthly budgets across Google Ads, Microsoft Ads, and Meta. Led an international North America campaign for Pirelli Tires that increased unit sales 37% in under a year, and has a track record of using rigorous testing and attribution to reallocate spend (including shifting budget from outdoor to digital to fill a luxury apartment building). Experienced advising automotive clients through 2025 market headwinds and retaining major accounts by translating complex performance data into decisive action.”

Steven Zavala

Screened ReferencesStrong rec.

Director-level growth marketer specializing in DTC e-commerce and CPG

Los Angeles, CA10y exp

“Lifecycle/CRM marketing leader from the wine and beverage space who has owned high-volume email/SMS programs end-to-end and translated CRM insights into broader revenue strategy. Stands out for combining rigorous process design, cross-channel automation, and personalization testing to drive measurable gains, including a 45% lift in incremental email revenue and $1.6M in incremental on-site upsell/cross-sell revenue.”

Cassie Sanborn

Screened ReferencesStrong rec.

Director-level Product Leader specializing in consumer growth and digital platforms

Rio Dell, CA13y exp

“Product leader with strong consumer and education-oriented product experience, including personalization, trust/safety moderation, and AI-enabled workflows. Stands out for pairing user research with measurable outcomes—such as boosting Spanish review output by 165% through AI translation—and for a thoughtful philosophy that AI should amplify human judgment and support rather than replace it.”

“Performance marketer managing a $500K/month paid media portfolio across Google, Microsoft, Meta, TikTok, Reddit, YouTube, and Quora with a focus on profitable growth. Uses a rigorous testing framework across creatives, bidding, and landing pages (leveraging Adobe Analytics and customer journey dashboards) and cites 200–400% YoY revenue growth plus 200–300% ROAS improvement; recently restored 20–30% of lost conversions at Labcorp within two weeks after diagnosing competitive and seasonal shifts.”

Rachel Downey

Screened ReferencesStrong rec.

Mid-Level Game Designer specializing in systems and economy design

Livermore, California4y exp

“Game economy/progression designer with hands-on ownership of end-to-end systems and live-ops tuning on major mobile titles (Star Trek Fleet Command, Marvel Contest of Champions). Builds spreadsheet-based simulations and telemetry-driven tuning loops to prevent inflation and reduce progression friction, including a multi-iteration optimization that lifted D7 retention ~7% while maintaining monetization targets.”

Anthony Hernandez

Screened ReferencesStrong rec.

Mid-level Game Designer specializing in systems, balance, and live updates

Los Angeles, CA6y exp

“Game designer currently leading design on a Mario Party-like party game, owning mode design, economy, and meta-progression. Uses playtests and A/B tests to tune fairness-focused systems (e.g., dynamic power-ups accounting for player standing and turn order) and prototypes quickly in UE5 Blueprints. Previously shipped a mixed-reality version of Knockout League’s Knockout Alley mode for a Quest 3-aligned release.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Interned at Larsen & Toubro where they built and deployed an agentic RAG document question-answering system to reduce time spent searching documents and improve trustworthiness. Implemented ReAct-style multi-step orchestration with LangChain/LlamaIndex plus evidence-bounded generation, grounding/citations, and rigorous evaluation—cutting latency ~40%, hallucinations ~35%, and unsafe outputs ~40% while collaborating closely with non-technical business/ops stakeholders.”

“Frontend engineer at Mailchimp who leads end-to-end React/TypeScript features on the in-app homepage, including onboarding and campaign discovery components. Demonstrated measurable performance impact by cutting homepage LCP by ~2.5s and successfully shipped a major feature on an accelerated deadline using structured QA and staged rollout.”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”