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Vetted Data Cleaning Professionals
Pre-screened and vetted.
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Junior Research Data Scientist specializing in healthcare analytics and real-world evidence
Mid-level AI/ML Engineer specializing in Generative AI agents and enterprise analytics
Mid-level Data Analyst and Business Analyst specializing in BI, reporting, and analytics
Mid-level Data Analyst specializing in financial analytics and regulatory reporting
Intern-level economist and strategy analyst specializing in econometrics and macroeconomic research
Intern data and technology analyst specializing in analytics and IT systems
Mid-level Full-Stack Software Engineer specializing in type-safe systems
Entry-level Software Engineer specializing in AI systems and embedded computing
Senior Software Engineer specializing in embedded systems, simulation, and data science
Senior Software Engineer specializing in FinTech and compliance systems
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Mid-level Data Engineer specializing in financial data engineering and scalable pipelines
Senior RPA Developer/Analyst specializing in UiPath automation and enterprise integrations
Mounika Nadendla
Senior Data Engineer specializing in cloud data platforms and real-time streaming
“Data engineer focused on building reliable, production-grade data systems end-to-end: batch and real-time pipelines (Airflow/Kafka/Spark) with strong data quality, monitoring/alerting, and incident response. Has experience integrating external API/web data with retries, throttling, and schema-change handling, and serving curated datasets to analytics (Power BI) and backend consumers with performance optimizations like Redis caching.”
Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”
Nightvid Cole
Senior Computer Vision & Sensor Algorithms Engineer specializing in imaging systems
“Robotics/remote-sensing software engineer who built and validated multisensor image-processing and spectral chemical-detection pipelines (RX anomaly detection, ACE), including calibration protocols with a motorized shutter and rigorous data QC. Uses white-box NumPy simulators to debug SLAM/registration issues before translating logic to C++, and partnered with hardware teams to solve temperature-driven signal variation via combined software calibration and improved thermal management.”
Alicia Chaney
Senior Talent Acquisition & Talent Development Leader specializing in early-career and workforce transformation
“Talent/Recruiting Operations leader who has managed teams up to 6 and specializes in fixing broken interview pipelines through workflow standardization, scheduling automation, and ATS analytics. Built real-time dashboards in Lever/ATS to track aging, time-in-stage, and req health, and drove a 20% reduction in time to interview while improving candidate experience and hiring manager visibility. Experienced leading cross-functional tool/workflow implementations with HRIS and IT for high-volume early-career hiring.”
Mid-level Software Engineer specializing in FinTech and cloud backend systems
Mid-level AI/ML Developer specializing in FinTech fraud detection and GenAI assistants
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Jayeetra Bhattacharjee
Mid-level AI/ML Engineer specializing in LLMs, NLP, and analytics automation
“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”