Vetted Data & Analytics 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.”

“Long-tenured EdTech operator who joined LinkIt! as the third Data Analyst when the company had fewer than 40 employees and grew into a Data Quality Manager over 7+ years. Stands out for blending data operations leadership—ETL migration, reporting automation, process documentation, and training—with highly hands-on startup execution across office operations, hybrid work rollout, and crisis response during COVID.”

“Frontend engineer with experience redesigning high-stakes travel checkout flows at Spotnana and improving payment-page performance at RedViolet. Stands out for turning flight-specific, monolithic React code into reusable modular form architecture and delivering measurable UX gains like a 20% LCP improvement.”

“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“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.”

“Customer-facing data engineering professional who builds and deploys real-time reporting/dashboard solutions, gathering reporting and compliance requirements through direct stakeholder engagement. Experienced with Google Cloud IAM governance, secure integrations (encryption, audit logging), and fast production troubleshooting of ETL/pipeline failures with follow-on monitoring and automated recovery improvements; motivated by hands-on, travel-oriented customer work.”

Bharath Lingireddy

Screened

Mid-level Supply Chain Analyst specializing in strategic sourcing and supply chain analytics

Spring, TX5y exp

“Sourcing/procurement professional focused on electronic components, leading end-to-end RFQ and supplier selection through negotiation and delivery. Demonstrates strong data-driven cost management (Power BI modeling, benchmark pricing) and measurable results including ~18% cost reduction, avoiding production delays during shortages, and automating RFQ comparisons to cut cycle time ~40%.”

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.”

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.”

“Data engineer focused on marketing/web analytics and external API pipelines, handling ~10M records/week. Built Azure-based ingestion and PySpark transformations with rigorous data quality checks, then served curated datasets into Synapse/Redshift for Power BI. Also designed an Airflow-orchestrated crypto REST API pipeline with monitoring, retries/exponential backoff, schema-change detection, and backfill-friendly reprocessing.”

“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.”

“Worked on a P&C insurance integration project at Accenture, using SQL to unify policy, billing, and claims data from APIs and ETL pipelines into clean reporting tables. Demonstrated hands-on experience with data quality validation, window-function-based transformations, and query performance tuning, helping business teams get a single reliable view for faster claims processing and management reporting.”

“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.”

“Analytics professional from Capital One with strong experience automating risk, reconciliation, and regulatory reporting workflows in financial services. They combine deep SQL/Python pipeline skills with stakeholder-facing dashboard and KPI design, delivering measurable impact like 30% performance gains, sub-24-hour anomaly detection, and 100% data integrity for regulatory filings.”

Harshith Jaladi

Screened

Entry-level Software Engineer specializing in FinTech distributed systems

Chennai, India1y exp

“Game developer with early-stage startup experience who worked directly with a CEO to integrate an AI-based API into Skyrim Elder Scrolls V, helping showcase the product and win Riot Games as a client. Currently owns multiple financial reporting ingestion workflows and has driven meaningful time savings through cross-functional execution, combining gaming/AI experience with operational impact in fintech.”

“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.”

“Analytics candidate with strong finance/operations reporting experience, focused on billing, margin, and revenue leakage use cases. They have built SQL and Python workflows that turned messy shipment, invoice, PO, and ERP data into trusted reporting layers, replacing manual Excel processes and driving adoption through transparent reconciliation and stakeholder collaboration.”

“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.”

“Built a production multimodal LLM-based vendor risk assessment platform that ingests SOC reports and other documents, uses a strict RAG pipeline with grounded evidence (page/paragraph citations), and dramatically reduces analyst review time. Experienced with LangGraph/LangChain/AutoGen for stateful, fault-tolerant agent workflows, and emphasizes reliability (schema validation, guardrails) plus low-latency delivery (~1–2s) through hybrid retrieval, reranking, caching, and model tiering.”

“Data engineer (~4 years) with full-stack delivery experience (Next.js App Router/TypeScript + React) building a real-time operations monitoring dashboard backed by Kafka and orchestrated data pipelines. Strong production focus: Airflow + CloudWatch monitoring, automated Python/SQL validation (99.5% accuracy), and CI/CD with Jenkins/Docker; has delivered measurable improvements in latency, pipeline reliability, and query performance (Postgres/Redshift).”

“Data engineer focused on sales/marketing analytics pipelines, owning ingestion from CRMs/ad platforms through warehouse serving and dashboards at ~hundreds of thousands of records/day. Built reliability-focused systems including dbt/SQL/Python data quality gates with alerting, a resilient web-scraping pipeline (retries/backoff, anti-bot tactics, schema-change detection, backfills), and a versioned internal REST API with caching and strong developer usability.”

“Data engineer with Wells Fargo experience owning an end-to-end lakehouse ETL pipeline on Databricks/Azure Data Factory, processing ~480GB daily and implementing robust data quality/reconciliation across 40+ tables to reach ~99.3% reliability. Strong in performance optimization (cut runtime 5.5h→3.8h), CI/CD and monitoring, and resilient external/API ingestion with retries, schema validation, and backfills.”