Vetted A/B Testing Professionals

“Built a live free-to-play web-based financial market simulation game (YouTube-channel stock market) as a hobby project, now serving 3000+ users. Implemented core F2P systems including gacha, timed progression gates, and analytics-driven monitoring of engagement and economy health using a TypeScript/Python stack with Redis and Postgres.”

“JavaScript/React performance-focused engineer who contributed upstream to an open-source virtualization/pagination library, fixing overlapping-fetch race conditions and introducing prefetch/deduping patterns that cut load times from ~3s to <900ms and reduced render thrash ~35%. Also built healthcare automation systems (clinical summary and claims triage), including a FastAPI + RAG pipeline that retrieved CPT/ICD evidence, improving decision accuracy from 67% to 86% and reducing turnaround time by 40%.”

“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”

“ML/NLP engineer with experience at Charles Schwab building an NLP + graph (Neo4j) entity-resolution system to unify fragmented user/device/transaction data and improve downstream model quality and analyst querying. Has applied embeddings (SentenceTransformers + FAISS) with domain fine-tuning to boost hard-case matching recall by ~12% while maintaining precision, and has a track record of hardening scalable Python/Spark pipelines and productionizing fraud models via A/B tests and shadow-mode monitoring.”

“Enterprise Customer Success professional (Iron Mountain Services) who owns accounts end-to-end from onboarding through renewal, with a strong focus on driving adoption via success plans, stakeholder alignment, and integration unblocking across Product/Engineering/Sales. Also has adjacent martech/analytics exposure (Google Analytics, Search Console, SEO audit tools) and experience translating customer feedback and usage data into roadmap-impacting product requirements.”

“Financial data engineer who has built and operated high-volume batch + streaming pipelines (200–300 GB/day; 5–10k events/sec) using AWS, Spark/Delta, Airflow, Kafka, and Snowflake, with strong emphasis on data quality and reliability. Demonstrated measurable impact via 99.9% SLA adherence, major reductions in bad records/nulls, MTTR improvements, and significant latency/runtime/query performance gains; also built a distributed web-scraping system processing 5–10M records/day with anti-bot and schema-drift defenses.”

“Data engineer focused on AWS + Spark/Databricks pipelines, including an end-to-end nightly loan-data ingestion flow (~2.2M records) from Postgres/S3 through Glue and Databricks into a DWH with layered validation and alerting. Also built real-time streaming with Kafka + Spark Structured Streaming and a master’s project streaming Reddit data for sentiment analysis under ambiguous requirements and tight budget constraints.”

Ellise Kim

Screened

Mid-level CRM & Lifecycle Marketing Manager specializing in email/SMS growth for DTC brands

Los Angeles, CA7y exp

“CRM/lifecycle marketer at Honest who led end-to-end launch strategy for a new anti-aging collection, combining creative storytelling with a custom Y2K-inspired early-access experience. Delivered a 26% MoM lift in new subscribers and 36% waitlist-to-purchase conversion, and uses rigorous segmentation and A/B testing to tailor messaging across acquisition and retention—especially effective during periods of reduced paid media.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Gustavo Lander

Screened

Mid-level Marketing & Data Analyst specializing in BI, sales planning, and process automation

Madrid, Spain6y exp

“Partnership and growth professional with experience closing 5+ sponsorship/partnership deals for a flagship conference (LPN Congress & Expo) through cold outreach and tailored packages, then measuring ROI via post-event lead/sales tracking. Also brings CPG and B2B marketing experience from Mondelez and agriNews, including data-driven in-store GTM diagnostics and iterative lead-gen funnel optimization (landing page + social + email).”

Rahul Setia

Screened

Director-level Growth Marketing leader specializing in full-funnel acquisition and lifecycle optimization

Calgary, AB11y exp

“Growth and partnerships professional with experience across fintech (SMB loans) and edtech (ByteLearn), combining SEO-led content ecosystems and performance marketing experimentation with rigorous lead-quality verification. Has closed white-label partnerships in the dental industry and built creator/referral incentive programs, consistently tying initiatives to funnel metrics like leads, MQL/SQL, activations, LTV, and ROAS.”

Vincent Osere

Screened

Senior Operations Analyst specializing in business intelligence and financial services

Massachusetts, USA15y exp

“Analytics-focused candidate with hands-on experience turning messy datasets into reporting-ready outputs using SQL, building reproducible Python workflows, and operationalizing metrics in R Shiny dashboards. They stand out for combining structured data analysis with NLP and segmentation in marketplace-style datasets such as Airbnb, real estate, and sports salary data to drive pricing, engagement, and demand insights.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

“Education strategy and product leader with deep math education expertise who helped transform a literacy/assessment-focused company into a more comprehensive, integrated math solutions provider. Particularly strong at embedding AI-driven insights into teacher workflows in a human-centered way, balancing personalization, usability, teacher autonomy, and educational equity.”

varunkumar jukanti

Screened

Senior Front-End Engineer specializing in enterprise UI architecture for FinTech

Jersey City, NJ7y exp

“Senior frontend engineer with 6 years of experience building enterprise-grade Angular/React applications in regulated industries, especially banking. Most notably worked on BLCM at Tasha Bank, a compliance platform that replaced fragmented Word/Excel SharePoint processes with a unified browser-based workflow for analysts and managers, and drove measurable performance and usability gains.”

“AI/full-stack product engineer currently owning Fleck Intelligent Survey Chatbot at E15, a production RAG analytics assistant embedded in Compass Group dashboards for 300+ field operators. Stands out for combining LLM orchestration, analytics engineering, and strong systems thinking—cutting hallucinated numeric answers from 14% to 2%, reducing backlog 62%, and previously delivering a low-level protocol redesign at Amadeus that cut P99 latency by 56%.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Performance marketer focused on scaling high-spend ecommerce paid media, with hands-on ownership of a ~$150K/month Google Ads account for Solawave. Known for disciplined account restructuring, feed optimization, and controlled testing across Google/Meta/TikTok—driving ROAS improvement from ~2.2 to ~5 in under three months while keeping spend predictable and scalable.”

“Content creator and growth marketer from the high-ticket wellness space who built a 30K+ IG / 1K+ YouTube community and drove $712K+ in affiliate sales. Runs end-to-end GTM using educational video, Meta/YouTube distribution, landing pages, and email nurture, optimizing via CPL and sales while mentoring creators through a paid training/enablement offer.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Growth/creative marketer from Esleydunn Games who uses Google Analytics to integrate cross-channel performance data (TikTok, YouTube, LinkedIn, Facebook) and run structured A/B tests on video ad length and layout. Reported reducing CPA by 20 per customer when leveraging YouTube and TikTok, and improved CTR through CTA/button placement testing and ongoing user-feedback loops (forum/WeChat topics).”

“LLM/agentic workflow engineer with healthcare domain experience who built a HIPAA-compliant multi-agent RAG system for clinical review automation at UnitedHealth Group, achieving 92% precision and cutting latency 40% through async orchestration and Redis semantic caching. Also has strong data engineering orchestration background (Airflow on AWS EMR with Great Expectations) and a proven clinician-in-the-loop feedback process that improved model faithfulness by 18%.”