Priyansh S.

Key Skills

Software

Other

CrowdSource

Data Annotation Tech

Google Cloud Vertex AI

HiveMind

Mercor

Scale AI

SuperAnnotate

Top Subject Matter

Financial markets and LLM-driven financial text signal extraction

Web3 adoption analytics and financial time-series/transaction feature engineering

Legal Services & Contract Review

Top Data Types

Text

Document

Image

Top Task Types

Prompt + Response Writing (SFT)

Data Collection

Fine-tuning

Text Summarization

Text Generation

Object Detection

Classification

Freelancer Overview

Research Lead & Data Scientist (Blockchain Business Lab). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, Stony Brook University (2026). AI-training focus includes data types such as Text and labeling workflows including Prompt + Response Writing (SFT) and Data Collection.

Labeling Experience

Undergraduate Researcher (VIP), Virtually Integrated Projects

OtherTextData Collection

Extracted historic market and on-chain transaction data to identify Web3 adoption trends using Python and SQL, emphasizing feature engineering for downstream model training. Led computation-intensive forecasting work by estimating ML models on partitioned financial/accounting and market features. Used HPC infrastructure to scale processing across massive datasets for reliable model training outcomes. • Performed data extraction and preprocessing for market/on-chain signals to support trend analysis. • Supported cross-sectional stock return forecasting using ML models trained on engineered features. • Scaled computations using Seawulf and NVwulf HPC cloud infrastructure. • Coordinated a 4-member team to deliver model training and signal discovery results.

2026 - Present

Research Lead & Data Scientist (Blockchain Business Lab)

TextPrompt + Response Writing (SFT)

Directed AI/ML research initiatives that build LLM-based agent workflows for extracting actionable signals from financial text. Built deep learning and ensemble forecasting models using systematic experimentation to improve predictive outputs from structured financial inputs. Designed automated evaluation and backtesting to validate the quality of generated signals before downstream use. • Managed a team of 16 students and coordinated research deliverables using Agile workflows. • Developed forecasting models (Random Forest, XGBoost, LSTM) with hyperparameter tuning on large financial datasets. • Implemented an agentic LLM environment with a custom agent architecture for signal extraction. • Enhanced simulated trading efficiency using vectorized backtesting and rigorous evaluation metrics.

2024 - Present

Education

Work History