Vetted Customer Segmentation Professionals

Gina Mayberry

Screened

Senior Digital Marketing & Paid Media Specialist specializing in PPC and growth marketing

Remote8y exp

“Performance marketer focused on B2B SaaS pipeline quality, managing a $90K/month spend across Google, LinkedIn, Meta (and TikTok in other accounts). Uses rigorous hypothesis-led testing with offline conversion/SQL-based optimization to scale efficiently—delivering +21% SQL growth at flat CPL and recovering stalled performance with a +25% SQL lift in 6 weeks.”

Wingho Lam

Screened

Mid-level Growth Marketer specializing in performance marketing and lifecycle funnels

Huntington Beach, CA3y exp

“Lifecycle/CRM marketer who built automated post-visit, re-engagement, and abandoned-cart programs with behavioral segmentation and UTM-based attribution. Delivered strong measurable impact in 90 days: +42% repeat visits, membership conversion from 3% to 15%, 1,500 five-star reviews, and growth to $100k MRR, while also aligning ops/front-desk messaging for a consistent customer journey.”

SASIREKHA GULIPALLI

Screened

Mid-level Data Analyst specializing in procurement, supply chain analytics, and applied machine learning

Alpharetta, GA4y exp

“Strategic sourcing professional specializing in seasonal apparel supply chains, combining Coupa/JD Edwards analytics with Excel/Python modeling and Power BI dashboards to drive cost reduction and OTIF gains. Notable for rapid mitigation of a 10-day factory delay affecting 12 holiday SKUs (preserved 95% of revenue) and for automating PO workflows to cut cycle time by 4.2 days and improve OTIF by 15%.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”

“Growth creative marketer who led end-to-end experimentation for Kahana’s Oasis agentic browser launch, repositioning it as a task-specific “productivity multiplier” and validating the message via structured A/B tests across Meta, LinkedIn, and landing pages. Reported performance lift included CPA reductions (23% Meta, 17% LinkedIn) and a 28% ROAS increase, with a repeatable modular framework for rapid creative iteration and hands-on direction of UGC creators and editors.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Growth and brand marketing leader with franchising experience who helped scale Xponential Fitness’ Rumble Boxing from 15 to 90 locations in 4 years, delivering a 30% lift in lead gen/conversions and hitting systemwide revenue targets. Built scalable omnichannel launch playbooks for new openings and created an influencer/creator engine that tripled creative output and cut CPL by 25%. Former CMO for a music-nostalgia-driven brand with strong perspective on creator authenticity and IP/credit in marketing.”

“B2B SaaS growth marketer who was the first marketing hire and built the foundational brand and acquisition engine from 0-to-1, using customer-journey mapping, rigorous channel testing, and strong cross-functional alignment. Has launched an existing product into a new adjacent market via a multi-channel campaign and optimized spend by prioritizing CAC, conversion, and lead quality. Also has experience marketing monetization/distribution platforms to creators and developers, emphasizing values-aligned messaging for the creator ecosystem.”

“ML/LLM engineer at Charles Schwab who built a production loan-advisor chatbot integrated with internal knowledge and loan-calculator APIs, adding strict numeric validation to prevent rate hallucinations and optimizing context to control costs. Also runs ~40 Airflow DAGs orchestrating retraining/ETL/drift monitoring with an automated Snowflake→SageMaker→auto-deploy pipeline, and uses rigorous testing plus canary rollouts tied to business metrics and compliance constraints.”

“Operations/HR leader and advisor with hands-on experience scaling an FDA-regulated manufacturing operation after a national retail placement—driving >40% throughput gains, 60% defect reduction, and cutting turnover by more than half through workforce, compensation, and training system redesign. Also led HR process automation (ATS/HRIS integration, employee self-service) and coaches executives on delegation, accountability, and outcomes-based operating rhythms.”

SUMIT MAMTANI

Screened

Mid-level Data Scientist specializing in ML, MLOps, and customer analytics

Tempe, AZ4y exp

“ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.”

Pravalika Kasojjala

Screened

Mid-level AI/ML Engineer specializing in LLM, RAG/GraphRAG, and fraud analytics

Charlotte, NC5y exp

“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”

“Analytics-focused candidate with experience owning end-to-end data projects across AI transcription, retail forecasting, and transportation revenue analytics. They combine strong SQL/Python pipeline skills with dashboarding and stakeholder alignment, citing measurable impact including 60% lower ETL latency, 18% better forecast accuracy, and 25% operational efficiency gains.”

“Healthcare-focused data analyst with hands-on experience at Molina Healthcare building SQL and Python workflows for retention and churn analytics. They combined enrollment, CRM, and claims data into Power BI reporting, automated predictive churn analysis, and tied their work to measurable outcomes including faster processing, better reporting accuracy, and reduced churn.”