Vetted Risk Management Professionals

“Global people/program leader with experience managing teams across Seoul, Tokyo, and Brazil; addressed language and collaboration barriers via workshops and cross-functional workflow design, driving a major attrition improvement and 90% team approval plus board commendation. Has led edtech product teams (design + development) through a 4-month sprint and partnered with executives on org-wide onboarding redesign using 30-60-90 plans and workflow data.”

“Operations/PMO leader across IPG Mediabrands, Kinesso, and IPG Corporate with deep experience building executive governance systems and cross-functional operating rhythms. Led high-visibility transformations including a $36M global rebrand and created data-backed dashboards/cadences that improved efficiency (10–20%) and executive alignment, including oversight of a 175M monthly media spend analysis.”

“Previously supported capital raising at Space Ocean Corp by securing several thousand dollars in grant funding and building investor-readiness/outreach materials. Has early exposure to VC/studio/accelerator meetings and articulates nuanced differences in their incentives and time horizons. Interested in refining skills as an early-stage CTO and is highly committed to entrepreneurship.”

“CFO for PE-owned Catalyze Holdings (distributed generation solar + battery storage for C&I) brought in to professionalize a founder-led startup into an IPO-ready organization. Implemented IPO-readiness governance plus Procore and NetSuite (on time/on budget), established KPI and stage-gate operating rhythms, and drove change management that improved process clarity and reduced G&A spend.”

“Operations/finance leader with experience building the operational backbone for fast-growing firms, including setting up recordkeeping, internal controls, and brokerage-specific clearing firm decisions. Helped scale an entity from ~$30M to nearly ~$300M and later modernized consulting-firm expense operations by implementing Concur integrated with ERP, with a hands-on approach to systems selection and change management.”

Keith Aumiller

Screened

Director-level Data & AI Leader specializing in enterprise transformation

Philadelphia, PA22y exp

“Built Cigna Health’s risk-based management/monitoring system used as a primary product for pharmaceutical clients, supporting clinical trials and utilized during the COVID-19 pandemic. Currently running a community-based startup (details kept proprietary) and is open to executive roles or joining a strong team with a clear path to success, evaluating opportunities via ROI.”

Priyanshu Maurya

Screened

Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics

New York, NY3y exp

“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”

Aigo Madakimova

Screened

Senior Data Analyst specializing in audit analytics, automation, and financial data platforms

Malvern, PA6y exp

“Full-stack engineer with strong Next.js App Router + TypeScript experience who built and owned a production internal analytics dashboard end-to-end, including server-component data fetching, route handlers for secure proxying, and post-launch monitoring/caching fixes. Also designed Postgres data models and performance-tuned analytics queries, and built reliable BullMQ/Redis-based order-fulfillment workflows with idempotency, retries, and compensating refunds—comfortable operating with high ownership in early-stage teams.”

Pratik Jaiswal

Screened

Mid-level AI/ML Engineer specializing in financial services ML and MLOps

Remote, USA4y exp

“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.”