Vetted Data & Analytics Professionals

“CRM/lifecycle marketer with hands-on ownership of high-volume, multi-channel programs across email, SMS, and push, including Braze journey design, QA, deployment, and post-campaign analysis. Stands out for combining strong campaign operations with incrementality measurement and experimentation, including a 20% conversion improvement from journey optimization and 12% incremental revenue lift identified through holdout-based analysis.”

“Analytics candidate with hands-on experience turning messy pricing and competitor data into reporting-ready SQL tables, plus building Python automation workflows that replaced manual processing across 40,000 images at roughly 89% accuracy. They also led a price elasticity analysis that informed differentiated pricing strategies and supported reporting through Power BI dashboards.”

“Analytics professional with Deloitte experience building SQL and Python workflows for revenue, pipeline, and opportunity analytics at scale. They combine strong data engineering and modeling skills with business-facing delivery, citing impacts including 8-10% conversion improvement, ~$700K revenue protected, 12% YoY project acquisition growth, and 15% retention improvement in financial services.”

“Founder/CEO of a consumer products startup since 2020 who has raised angel funding and reinvested it into R&D, product development, production, and expert legal/industry support (has not taken a salary). Former financial and deals consultant who applies rigorous market, buyer-landscape, and exit-path analysis (including Blue Ocean Strategy) to choose and execute opportunities, with a strong commitment to entrepreneurship.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Supreetha Kashyap

Screened

Mid-Level Software Engineer & Data Analyst specializing in cloud analytics and BI

Jacksonville, FL4y exp

“Built and owned an end-to-end Seat Allocation & Management System at Accenture, replacing a legacy process with a scalable web app used across teams. Deep focus on reliability under concurrency (transactions + unique constraints + idempotent APIs) and on Postgres performance tuning (composite indexes, EXPLAIN ANALYZE), plus post-launch production support and monitoring.”

Samatha Amsala

Screened

Mid-level Data Engineer specializing in cloud data warehousing and analytics

Omaha, NE6y exp

“Data engineer at American Express who owned end-to-end pipelines for transaction and customer data used in finance reporting and risk analytics, processing ~5–8M records/day. Built Airflow-orchestrated ingestion (including external APIs/web sources) with strong data quality controls, monitoring/alerts, and resilient backfill/retry patterns, and also shipped a versioned REST API serving aggregated metrics to analytics teams.”

“Analytics-focused candidate with hands-on experience using SQL, Python, and Power BI to turn fragmented event and user data into reliable reporting layers, automated workflows, and stakeholder-facing dashboards. They appear strongest in product/growth analytics, especially funnel, conversion, retention, and user behavior analysis, with clear examples of driving product improvements through metric design, segmentation, and ongoing monitoring.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“ML/NLP practitioner with experience building a transformer-failure prediction system that combines sensor signals with unstructured maintenance comments using LLM-based extraction and similarity validation. Strong emphasis on production readiness—data leakage controls, SQL-driven data quality tiers, and rigorous bias/fairness validation (including contract/spec evaluation across diverse company profiles).”

“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”

“Data engineer with experience delivering an end-to-end pipeline handling ~3.5TB in a star-schema setup (fact + dimensions) and producing business-facing tables in Hive/Spark. Identified and resolved UAT-reported duplicate issues caused by joins through root-cause analysis, and also built automation to run Spark SQL metrics on weekly/monthly/quarterly cadences and distribute results to users.”

“Data engineer with experience at Sony and Walmart building high-volume, near-real-time analytics and ingestion systems. Has owned end-to-end pipelines from Kafka/Spark streaming through S3/Parquet and Redshift/Looker, emphasizing data quality (Great Expectations), observability (CloudWatch/Azure Monitor), and reliability (Airflow SLAs, retries, checkpointing), including measurable performance and latency improvements.”

“Data engineer focused on fraud and banking analytics who has owned end-to-end batch + streaming pipelines at very large scale (hundreds of millions of records/day). Built robust data quality/observability layers (schema validation, anomaly detection, alerting) and delivered low-latency serving via AWS Lambda/API Gateway with DynamoDB + Redis, plus external data ingestion/scraping pipelines orchestrated in Airflow with anti-bot protections.”

Abhinav Gupta

Screened

Junior Machine Learning Engineer specializing in LLMs and applied data science

2y exp

“Built and shipped multiple production AI systems, including Auto DocGen (LLM-generated OpenAPI docs kept in sync via AST diffs, schema-constrained generation, and CI/CD on Render) and a multimodal sign-language recognition pipeline at USC orchestrated with FastAPI, MediaPipe, and PyTorch. Also partnered with Esri’s non-technical community team to fine-tune an LLaMA-based spam classifier with a review UI, cutting moderation time by 70%.”

Bhanu Prakash Reddy Dakilli

Screened

Mid-level Data Engineer specializing in Azure ETL/ELT and data warehousing

Framingham, MA4y exp

“Data engineer who has owned end-to-end production pipelines for customer transaction data (~2–5 GB/day) using Python/PySpark/SQL and Airflow, delivering major reliability and speed gains (70% faster reporting; 60–70% fewer data issues). Also built a daily external web-scraping system with anti-bot handling and safe, idempotent Airflow-driven backfills, plus a Python data API optimized with indexing/caching and tested for correctness.”

Ruthvik Bacha

Screened

Mid-level Data Engineer specializing in financial data pipelines and reliability

North Carolina, USA7y exp

“Systems/robotics-oriented software engineer focused on real-time orchestration and reliability: built a central control layer coordinating multiple concurrent agents with safe state machines, failure isolation, and recovery. Has hands-on ROS/ROS 2 integration experience in simulation (DDS/QoS, lifecycle, nodes in Python/C++) and emphasizes observability (structured JSON logs, correlation IDs) and low-latency control-loop performance under load.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

“Analytics candidate with hands-on experience at AT&T building SQL/Python pipelines for churn, usage, billing, and network-performance data at multi-million-row scale. Stands out for combining strong data quality and reconciliation practices with measurable operational impact, including a 30% query runtime improvement and ~8 hours/week of reporting automation savings.”

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“Backend/data platform engineer who recently owned an end-to-end large-scale financial data platform delivering real-time decision support for finance and operations. Has hands-on experience modernizing legacy batch pipelines into AWS cloud-native ELT with parallel-run cutovers, strong data quality controls (dbt-style tests, reconciliation), and measurable improvements in runtime, cost, and SLA compliance. Also builds scalable, secure FastAPI microservices using Docker, ALB-based horizontal scaling, Redis caching, and managed auth with Cognito/Supabase plus Postgres RLS.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Built and productionized an LLM-powered PDF document Q&A system to eliminate manual searching through long documents, focusing on scalability and answer reliability. Implemented semantic chunking (using headings/paragraphs/tables), overlap, and preprocessing/quality checks to reduce hallucinations, and orchestrated the end-to-end pipeline with Airflow using retries, alerts, and parallel tasks.”