Vetted Statistical Analysis Professionals

Emily Hottal

Screened ReferencesStrong rec.

Director-level Product Leader specializing in AI, data, and customer engagement SaaS

Stow, MA17y exp

“Senior product leader at Airship driving both major platform modernization and the company's AI transformation. They led a contact-centric rebuild touching nearly the entire customer engagement platform, then built an AI recommendations product on Vertex/Gemini with a design-partner rollout model. They also bring rare depth across analytics, experimentation, personalization, and email marketing, plus experience rebuilding PM teams and shaping product strategy with executive leadership.”

Sandhya Yadavalli

Screened

Mid-level Cybersecurity Analyst specializing in SIEM, incident response, and Zero Trust

Remote, USA3y exp

“Cybersecurity/SOC-focused engineer with hands-on production experience integrating and tuning Splunk Enterprise Security for a zero-trust program, including CIM normalization, correlation/risk-based detection tuning, and performance optimization via forwarder-level filtering and index strategy. Has cross-disciplinary incident troubleshooting experience spanning SIEM, networking, and hardware, and has automated customer/team-specific security workflows using Python in Splunk playbooks; collaborated on-site with HSBC IT/SOC teams to deliver dashboards and security policies.”

“AI product leader with experience at Bloomberg Tax and Sun Automation, focused on turning complex enterprise workflows into approachable, human-in-the-loop AI experiences. Notable for redesigning a technical data integration product around natural language and guided validation, driving a 72% reduction in onboarding/adoption time while emphasizing trust, transparency, and user control.”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

“Analytics professional at TIAA with hands-on experience combining SQL, Python, and statistical modeling to unify complex marketing, product, finance, and customer datasets. Has worked on advisor-tool adoption analysis, 10-year wealth diagnostics, forecasting, cohort analysis, and escalation-risk modeling, with findings used by marketing and contact-center stakeholders.”

“Analytics professional with healthcare and operations experience who turns messy enterprise data from platforms like Teradata, GCP, SQL Server, and Snowflake into trusted reporting layers and reproducible analysis workflows. They combine SQL, Python, PySpark, Power BI, and Tableau to improve reporting accuracy and performance, including a 30% dashboard refresh improvement and 20-25% accuracy gains in healthcare reporting.”

“Analytics professional with Dell experience unifying global online sales, web analytics, SAP, and planning data across 20+ countries into scalable reporting pipelines and Power BI dashboards. Stands out for combining deep SQL/ETL work with Python automation, KPI design, and experimentation—delivering measurable outcomes like 80% less manual effort, a 2% conversion lift worth millions, and faster business decision-making.”

“Built and owned an enterprise LLM/RAG document intelligence platform for PNC Financial Services in a compliance-heavy environment, focused on grounded answers over internal finance and policy documents. Stands out for combining GenAI product delivery with production engineering discipline, delivering 60% faster document review and materially better answer quality while creating reusable FastAPI-based AI services for multiple teams.”

Omkar Lahurikar

Screened

Senior engineering founder specializing in AI systems and automotive controls

Dearborn, MI13y exp

“Builder-minded founder/operator currently at Ford who previously co-founded MeruDynamics, an "Uber for manufacturing" platform, and took it from zero to paying customer without raising capital. He also independently built and deployed RDD, an AI copilot for engineering decision capture, with evals, prompt versioning, and LLM cost telemetry—showing unusual depth in both product thinking and practical AI execution.”

Anudeep Eloori

Screened

Mid-level Software Developer specializing in full-stack enterprise applications

USA3y exp

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”

Yaswanth Thota Thota

Screened

Mid-level Data Analyst specializing in financial risk and healthcare analytics

AZ, USA4y exp

“AI/ML engineer focused on real-time, production-grade LLM systems, with a robotics-adjacent mindset around latency/accuracy tradeoffs and modular pipelines. Built a scalable RAG-based assistant orchestrated as microservices on Kubernetes with Kafka async messaging, ONNX/quantization optimizations, and monitoring (Prometheus/Grafana), citing a ~35% hallucination reduction; has also experimented with ROS Noetic/Gazebo to understand ROS concepts.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

MOUNIKA SAI MEKALA

Screened

Junior Data Analyst specializing in financial and operational analytics

Kansas, USA3y exp

“Analytics professional with experience at KPMG turning messy operational and financial data from SQL Server and AWS S3 into clean reporting datasets and automated Python workflows. They combine SQL, Python, Power BI, and experimentation methods to deliver stakeholder-aligned KPI dashboards and marketing performance insights with a strong focus on data integrity and reproducibility.”