Vetted seaborn Professionals

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

Vivek Reddy

Screened

Mid-level Data Scientist/Data Engineer specializing in ML pipelines, insurance and healthcare analytics

Los Angeles, CA7y exp

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

“Analytics-focused candidate with hands-on experience building SQL reporting tables from messy transactional and master data, plus Python workflows that automate monthly analysis and data checks. They appear strongest in KPI/reporting ownership, metric standardization, and stakeholder alignment, with examples of improving reporting consistency, surfacing issues earlier, and reducing manual reconciliation effort.”

Hargun Kaur Kohli

Screened

Intern Business Analyst specializing in analytics and marketing insights

California, USA1y exp

“Graduate-school capstone and project work centered on analytics, including healthcare shipment/pharmacy data, customer recommendation modeling, and a gaming uplift modeling project. Stands out for framing analytics around business impact—using uplift, repeat purchase, and profit-oriented metrics rather than just accuracy.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”

“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”