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Vetted Customer Segmentation Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
Mid-level Business Analyst specializing in retail, healthcare, and aviation analytics
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Mid-level AI Engineer specializing in LLM agents, RAG, and enterprise GenAI
Junior Business Intelligence Engineer specializing in experimentation and causal inference
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
Executive Product & Technology Leader specializing in AI and healthcare platforms
Jennifer Marks
Executive Revenue & Go-to-Market Leader in Luxury Spirits and CPG
“Startup President in the liquor/spirits space with hands-on experience raising capital (from friends & family through family offices/venture) and leading a major pivot from DTC to wholesale. Built GTM plans, leveraged partnerships to secure distribution across 8 states, and launched an American Single Malt positioned as a category leader ahead of TTB ratification, including product/brand strategy and multi-market rollout.”
Surya Kumaraguru
Mid-level Product Lead and Customer Success Manager specializing in SaaS analytics and GTM
“Enterprise CSM with experience at IBM and Cloudera, focused on AI and cloud modernization engagements. Drives adoption, renewals, and expansion by building success plans with measurable ROI/usage outcomes, mediating complex stakeholder priorities, and unblocking deployments through tight Product/Engineering/Sales coordination. Also brings strong GTM analytics capability (Salesforce/HubSpot/GA/Tableau/SQL) to improve funnel performance and pipeline conversion.”
Murali Krishna Vempalli
Mid-level Data Analyst specializing in retention, churn, and customer analytics
“Analytics professional with experience across healthcare and fintech, including building SQL/Python data pipelines at Optum and owning a fraud detection initiative at Razorpay. Stands out for combining messy-data cleanup, reproducible analytics workflows, and stakeholder-driven metric design, with a reported 25% improvement in fraud detection while keeping false positives under control.”
Pratik Jaiswal
Mid-level AI/ML Engineer specializing in financial services ML and MLOps
“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”
Senior Data Scientist specializing in GenAI, LLM systems, and production ML
Mid-level Data Scientist specializing in machine learning, analytics, and cloud data pipelines
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level Data Scientist specializing in ML, MLOps, and forecasting for FinTech and AI hardware
Junior Software Engineer specializing in full-stack and cloud-native systems
Senior Marketing Technology Manager specializing in analytics, tracking, and CRM automation
Senior Cloud Security Engineer specializing in AWS and Azure security architecture
Vaughn Eugenio
Mid-Level Software Engineer specializing in distributed microservices and real-time systems
“Software engineer with production experience at DraftKings and SRC, owning high-impact platform changes like early-start lineup validation fixes and a multi-service refactor to support dual-role players (e.g., Ohtani) using backward-compatible, feature-flagged rollouts. Has embedded onsite with military users to rapidly ship improvements to a COP/TAK mapping integration (TrackSync), and leverages AI tools (Claude) to accelerate learning and delivery in new domains (e.g., ESP32 smart deadbolt project).”