Daniel S.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Cryptocurrency trading

fintech/blockchain market analysis

and trading data quality evaluation

Top Data Types

Text

Computer Code Programming

Image

Top Task Types

RLHF

Evaluation/Rating

Classification

Transcription

Data Collection

Freelancer Overview

Independent Cryptocurrency Trader & Bot Developer (Self-Employed). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Independent Cryptocurrency Trader & Bot Developer (Self-Employed)

Don't discloseText

Developed and deployed automated cryptocurrency trading systems and applied rigorous evaluation of trading signals and token quality based on multi-indicator criteria. Translated financial domain knowledge into rule-based judgment that can be used to rate AI outputs for accuracy, relevance, and correctness in crypto/fintech contexts. Acted as a domain rater for detecting misleading, incorrect, or hallucinated information in AI-generated trading and market analysis content. • Evaluated overbought/oversold conditions using RSI, StochRSI, MACD, and VWAP-style signal reasoning • Applied institutional-style filters such as VWAP rejection, liquidity grab detection, bearish divergence, and trend-strength checks • Implemented token quality and data validity filters including minimum age, liquidity thresholds, and volume validation • Produced quality-control style assessments suitable for financial content fact-checking and rating tasks

2022 - Present

Self-Directed Study — Cryptocurrency & Algorithmic Trading

OtherTextRLHF

Conducted self-directed study and experimentation in cryptocurrency and algorithmic trading with the intention of applying domain understanding to AI training and feedback workflows. Leveraged knowledge of derivatives, technical indicators, and cloud/API-driven data processing to support human-feedback style evaluation and RLHF-informed improvements for finance/fintech AI models. Built expertise that can be used for domain-specific annotation and quality rating of AI-generated financial summaries and analyses. • Studied technical analysis indicators and derivatives concepts to ground feedback and evaluation criteria • Used Python and API/data processing approaches to better interpret market-related information • Continued iterative learning for improved judgment on trading terminology, chart patterns, and market events • Prepared for AI training tasks involving finance accuracy evaluation, fact-checking, and annotation guidelines

2021 - Present

Education

Work History