Vetted Forecasting Professionals

“Tesla sourcing/supplier readiness professional focused on critical lithium-ion battery components, owning end-to-end supplier onboarding and delivery execution. Uses SQL and Power BI to run integrated readiness plans across samples/approvals/tooling and proactively mitigates cost and import-duty risks while improving supplier performance through structured execution cadences.”

Travoy Spelling

Screened

Senior Data Scientist / ML Engineer specializing in GenAI, LLMs, and NLP

Texarkana, TX10y exp

“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”

Surya Vardhan

Screened

Junior Operations Data Analyst specializing in KPI dashboards and SLA reporting

Seattle, WA1y exp

“Manufacturing/quality-focused professional with experience at Sikorsky Aerospace supporting aircraft parts production for clients such as Boeing and Cobham. Drove data-driven process improvements (cleaned/visualized production data) and redesigned material usage to cut delays by ~20% and reduce waste, while coordinating across production, inspection, QC, and delivery readiness.”

“Compensation and payroll professional with hands-on experience supporting end-to-end U.S. payroll and compensation cycles in Workday. Stands out for building Excel-based validation and reconciliation models, resolving HRIS/payroll/finance mismatches at the root-cause level, and adding controls that improve payroll accuracy and compensation decision-making.”

“Healthcare operator and entrepreneur who founded and scaled Tellica Imaging (an Intermountain Company) from inception to serving 150,000 patients, generating $70M+ in gross revenue, and operating across 4 US states. Serves as President & CEO of two joint ventures and has hands-on experience building board-ready business cases, securing internal capital, and mastering healthcare revenue cycle/reimbursement to drive growth and customer satisfaction.”

“Backend-focused software engineer (4+ years) across e-commerce, banking, and healthcare who owned mission-critical checkout/order management end-to-end and improved peak-traffic success rates via resiliency patterns (timeouts/retries/caching) and data-driven iteration. Also built and shipped real-time operational dashboards (React/TypeScript + Spring Boot) using WebSockets and event-stream integrations, with strong experience in Kafka/RabbitMQ-style messaging at scale.”

“Built and deployed a production RAG-based multi-agent chatbot during an internship to help consultants answer client questions and guide users through new IT systems with step-by-step instructions. Demonstrates hands-on experience with LangGraph/LangChain/Google ADK, unstructured document parsing and chunking for RAG, and a reliability-first approach to agent workflows (metrics, fallbacks, human-in-the-loop, guardrails).”

“Cross-functional operator with experience at Amazon and Airbus, known for building executive-facing systems (dashboards, AI-enabled project management, operating cadences) that improve communication and efficiency. Has handled high-pressure, multi-timezone priorities and customer-facing technical escalations using structured documentation and frequent stakeholder updates, while maintaining discretion in sensitive situations.”

Mayuresh Kanade

Screened

Director-level GTM and Customer Success leader specializing in Enterprise AI and SaaS

Redlands, CA18y exp

“Global sales and customer success leader with 20 years across the US, UK, and India who is now developing a buyer-first B2B platform focused on data sovereignty and shifting control from seller CRMs to enterprise buyers. Has worked with VC-backed startups and portfolio companies, including GTM efforts around AI/ML and cloud ecosystems such as Snowflake, Dataiku, Databricks, and Azure.”

“Former Division I baseball player who spent nearly seven years at the MLB Commissioner's Office overseeing the international signing process. Brings a rare combination of lived player experience, bilingual international recruiting work across Latin America, and a decade-plus network spanning every MLB club from GMs to academy administrators.”

“ML/NLP practitioner working across healthcare and business/finance use cases: currently fine-tuning a domain-specific Llama 3.1 model for safe reasoning over EHRs/clinical notes using RAG + RL/DPO and RAGAS-based evaluation. Has built UMLS-driven entity normalization pipelines with quantified quality gains and developed embedding/vector-DB systems (FAISS) for semantic matching and forecasting/recommendation applications at Aurora AI and Banxico.”

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Vedant Kharwal

Screened

Intern AI/ML Engineer specializing in Generative AI and applied machine learning

Mumbai, India1y exp

“New graduate with hands-on LLM work building a RAG pipeline (HNSW, lexical reranking/boosting, ReAct) and optimizing it through ablation to dramatically reduce latency. Also building a modular personal assistant with a custom wake word model, router-driven agent selection, and integrations like Spotify with secrets managed via .env.”

“Candidate combines fraud analytics experience at Citi with a clinical AI capstone involving reproducible ML pipelines for imaging and notes data. They stand out for turning messy, high-volume data into decision-ready reporting, automating evaluation workflows, and translating analytics into operational impact—from fraud rule changes to retention metric adoption.”

“Candidate brings a blend of consulting and brand-side marketing sourcing experience from Deloitte Consulting and L’Oreal. They stand out for using deep research, cost analysis, and one-on-one stakeholder engagement to drive agency negotiations, support a $4M annual savings target, and win adoption of new project tools like Smartsheet in change-resistant client environments.”

“Programmatic media specialist with deep hands-on experience running high-budget AWS/Amazon campaigns, including a $4MM side-by-side DSP bakeoff (DV360, The Trade Desk, Amazon DSP) focused on awareness and cost efficiency. Strong in CTV/OLV optimization and measurement, leveraging brand lift and attention studies, and known for disciplined testing frameworks and practical troubleshooting to restore performance.”

“Dynamics 365 Project Operations/Field Service implementer focused on operational execution—built foundational task tracking and expedited field-service scheduling with improved visibility, alerts, and billing/sales record traceability. Uses interviews plus pilot/testing to refine UI and business processes for adoption, and applies A/B testing techniques to both marketing emails and D365 form experiences while partnering with the D365 product team to address bugs and usability.”

Niyaz Nurbhasha

Screened

Mid-level Machine Learning Engineer specializing in computer vision and LLM pipelines

4y exp

“ML/LLM engineer who built production systems to speed up artist content-creation workflows, including a fine-tuned image captioning model paired with a RAG layer over image embeddings/captions to improve consistency across changing domains. Experienced orchestrating multi-tool agents with LangChain/LangGraph (planning + critic/reflection) and setting up practical monitoring (caption rejection rate) plus evaluation sets for tool-calling accuracy, output quality, and latency.”

“Performance marketing and ad operations specialist who owned an Eli Lilly high-spend account ($75K–$150K/month), running campaigns across GAM, FreeWheel, and Operative One. Focused on disciplined pacing, trafficking/QA, and cross-system reporting reconciliation to hit 100% in-full delivery, stabilize CPA mid-flight, and avoid makegoods or billing escalations.”

“Early-stage GTM and revenue-operations builder who helped Seculert (cybersecurity breach detection) scale beyond founder-led sales as US employee #3, standing up TAM/ICP targeting, first SDR/AE hires, Salesforce, and lead scoring/qualification systems. Experienced mentoring founders/executives with lightweight, stage-appropriate operating cadences and playbooks to create repeatable pipeline and scalable execution.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

“Enterprise Customer Success/Renewals leader who owned onboarding through renewal for large accounts (including a $500K ARR deal) and is described as the global leader for renewals in their org. Strong in executive stakeholder alignment, QBR-driven success planning, and structuring complex multi-year renewals/expansions (e.g., fintech ramped license growth with co-terming and pricing strategy) while influencing product roadmap via quantified ARR impact.”

“Retail buyer/category manager with experience rebuilding underperforming programs end-to-end through data-driven assortment strategy, supplier selection, and product/value engineering. Led a pet beds turnaround (70% sell-through in week one; category mix from ~20% to ~35% and primary IMU driver) and previously drove a licensed sports vendor at Amazon from a $550K projection to $1.7M by re-framing assortment planning around turn and product-type insights.”