Vetted Data Visualization Professionals

“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.”

“Engineer with hands-on experience designing and leading multi-agent AI development workflows, including a LangGraph-based system that automated parts of a RAG pipeline and significantly reduced development time. Stands out for treating AI agents like an engineering team, with clear architecture, handoff schemas, validation, and supervisor-driven conflict resolution.”

“Strategic sourcing/procurement professional with experience at Gumpro and Micron leading indirect (MRO/CapEx) and critical component sourcing initiatives. Uses structured category strategy (spend/Kraljic/Pareto), RFX, risk management, and TCO/should-cost models; reports $10M+ savings and lead-time reduction from 60 days to 2 days while mitigating geopolitical/tariff exposure and strengthening supply resilience.”

“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.”

“Senior Unity UI/UX developer who led XR interaction design for a VR meetings/events platform (VRChat/Horizons-like), including a modular radial menu that served as the app’s primary control system. Also built a Pokemon Go-style Unity AR experience for a major shopping centre end-to-end, tackling real-world marker scanning challenges (lighting, reflections, installation) through iterative prototyping and testing.”

“Robotics engineer who led core autonomy stack development at Spacer Robotics (Isaac ROS/ROS2) spanning sensor integration, SLAM/mapping, navigation, and validation. In a research lab thesis, built three mobile robots from scratch and created a distributed multi-agent collaboration framework with blockchain-based incentive models, demonstrating depth in both hands-on robotics and distributed systems.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

Hiya Kothari

Screened

Intern Full-Stack Software Engineer specializing in AI/ML and cloud

San Francisco, CA3y exp

“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”

Omkar Tulsidas Parab

Screened

Mid-Level Full-Stack Software Engineer specializing in web apps, data pipelines, and ML

San Francisco, CA4y exp

“Software engineer who owned an Order Management System end-to-end at Reliance Jio, improving large-table performance via UI virtualization shipped behind feature flags and refined through direct ops-user observation. Also built an OCR automation tool at Piramal Realty using Python/Tesseract with validation and manual correction fallbacks, driving adoption by operations teams. Experienced integrating with Kafka-based microservices and improving observability using structured logging and correlation IDs.”

Suraj Thangellapally

Screened

Junior Software Engineer specializing in machine learning and data science

San Jose, CA2y exp

“Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.”

Sri Vyshnavi Maganti

Screened

Senior Business Analytics Analyst specializing in product and customer analytics

Texas, USA7y exp

“Darwinbox team member who supported talent/recruiting operations while also driving product improvements across HR modules (recruitment, onboarding, payroll, performance). Led a small team (5–6) and implemented discovery-driven configuration and BI reporting (Power BI/Tableau/Confluence), including a reported 30% reduction in recruitment configuration issues and real-time funnel reporting to support fast hiring.”

Archana yaramala

Screened

Mid-level AI/ML Engineer specializing in deep learning, MLOps, and LLM applications

NY, USA4y exp

“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“Analytics-focused candidate with internship and project experience at Recotap and CoUnderscore, combining SQL, Python, and BI dashboards to turn messy marketing and engagement data into decision-ready reporting. Stands out for tying analytics work to business outcomes, including ~15% CTR improvement, identifying ~40% misattributed spend, and enabling a ~$75K budget shift through better targeting.”

“Analytics professional at Optum with hands-on experience turning messy healthcare claims data from SQL, Excel, and CRM systems into validated reporting datasets and Power BI dashboards. They also built reproducible Python workflows for claims analysis and owned an end-to-end project focused on improving claims processing efficiency through metric design, segmentation, and stakeholder-driven operational improvements.”

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Entry-level software engineer who independently built an AI-powered feedback aggregation and analytics dashboard end-to-end using Cloudflare Workers, D1, and React. Stands out for combining serverless backend design, LLM-based categorization, and thoughtful UI/UX polish, with a practical approach to production debugging and data reliability.”

“Analytics professional with hands-on experience in supply chain and logistics transformation, including enterprise data preparation in SQL, Python automation, and Power BI reporting. They highlight ownership of end-to-end digitization work at Blue Dart, where they defined operational metrics, aligned cross-functional stakeholders, and delivered measurable gains in transparency, reporting efficiency, and implementation quality.”

Sarfaraz Karim

Screened

Mid-level Finance Analyst specializing in product, analytics, and technology strategy

Chicago, IL4y exp

“Startup-oriented business development candidate who built outreach and pipeline processes from scratch for early-stage companies including a law firm and consulting-related work. Stands out for highly persistent, research-driven prospecting across social, email, phone, and even in-person outreach, including winning a hotel client after failed remote attempts by showing up with a tailored strategy.”

“AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.”

“ML practitioner with a QA background who has built end-to-end ML pipelines for a health risk prediction use case (lifestyle + demographics), emphasizing robustness through strict data validation, leakage prevention, and cross-validation. Collaborated with a dietician to sanity-check predictions and refine feature interpretation for real-world practicality; has not yet deployed LLM/AI systems to production and has no hands-on orchestration framework experience but is willing to learn.”

“Recruiting leader with 4 years managing a 25-person recruiting team and deep experience in C-suite executive search. Built a full recruiting lifecycle process from the ground up in their current role, scaling to ~60 candidate interviews per week and implementing Salesforce-based pipeline tracking. Regularly partners with HRBPs and works directly with CEO/COO on retention, budgeting, and hiring/firing decisions.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”