Vetted Tableau Professionals

“Product/full-stack engineer with analytics-dashboard experience at Kantar, owning features end-to-end from React/Next.js UI through Postgres data modeling and query optimization. Built a multidimensional filters/tags module that cut analyst discovery time by ~60% and also implemented durable backend workflows for bulk report generation with retries and idempotency, validated via EXPLAIN ANALYZE and production monitoring.”

“Production infrastructure engineer from Textron Systems who owned IBM Power/AIX 7.2 environments supporting manufacturing-critical automated RF test workloads. Deep hands-on experience with VIOS/HMC, DLPAR performance issues, SAN/vFC failures and failover recovery, plus modern DevOps practices (Azure DevOps CI/CD, Key Vault) and Terraform-based AWS infrastructure with remote state/locking and drift controls.”

“Worked on taking an LLM-based system at Soundmakr from prototype to production by adding prompt constraints, validation/guardrails, deterministic ranking, and robust logging/monitoring with feedback loops. Also partnered with product/marketing during an internship on Thea: Study Smart to analyze onboarding drop-offs and run A/B tests on AI-driven flows, translating results into actions that improved retention and conversion.”

“Enterprise healthcare SaaS implementation and customer success leader focused on OR/clinic scheduling optimization, owning end-to-end deployments from discovery through training, adoption, and renewal across multi-hospital systems. Strong analytics/ROI storytelling (Tableau/Looker, QBRs) linking AI-driven outreach and block release workflows to increased case volume and improved block utilization, and experienced partnering with Product/Sales to resolve integrations and drive roadmap/upsell outcomes.”

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Performance marketer managing high-spend, multi-platform paid media for major brands (e.g., Navistar, Buick), with end-to-end ownership from strategy to reporting. Uses multivariate testing and trend-based budget reallocations to drive measurable lifts (27–60% KPI improvements) and significant outperformance (2.8x vs forecast), and has experience removing conversion friction by replacing lead forms with landing page + chat-based flows.”

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Lifecycle/CRM marketer with hands-on Braze automation experience, running cross-channel (email + push) onboarding and milestone campaigns for an app-based content product. Demonstrated strong activation metrics on Day 0 and drove a 10% week-over-week churn reduction by partnering with Customer Service on winback save-rate and scripting tests.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

“Built and deployed a production multimodal language translation platform (text-to-text, speech-to-text, text-to-speech) using fine-tuned pretrained models (NLLB, XLSR), MLflow-orchestrated pipelines, and Docker/Kubernetes on AWS. Worked closely with non-technical linguists to tackle data cleaning and dialect variation in minority languages, improving accuracy through consistent evaluation and monitoring.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“ML/AI engineer (TCS) who built and productionized a customer segmentation and personalized-offer recommendation pipeline end-to-end (data cleaning/feature engineering/clustering through Flask API deployment in Docker with monitoring). Emphasizes reliability and operational rigor via validation checks, periodic retraining, model/API versioning, and latency optimization, and has experience translating marketing KPIs into usable dashboards for non-technical teams.”

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

“Full-stack engineer with experience at Cummins owning production features end-to-end (React/TypeScript + Node + Postgres) and operating them in AWS (EC2/RDS/S3/IAM) with CloudWatch-based observability. Also built resilient ETL and third-party integrations, including an AWS Glue–S3–Redshift pipeline hardened with validation, idempotent UPSERTs, retries/backfills, and quarantine handling to prevent bad or duplicate data.”

“Worked on large financial analytics datasets combining complaint text, transaction logs, and demographics; built end-to-end NLP/ML pipelines (TF-IDF + Random Forest) and data integration in BigQuery with Tableau reporting, citing ~95–98% accuracy. Also implemented entity resolution with fuzzy matching and semantic linking using BERT sentence-transformer embeddings stored in FAISS, including fine-tuning on labeled pairs to improve search/linking relevance.”

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”