Rishikesh O.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Quantitative finance / stock price prediction

Trading analytics / forecasting

Market data enrichment / analytics

Top Data Types

Document

Image

Video

Top Task Types

Data Collection

Freelancer Overview

Varilligence Technologies, Data Scientist (AI training / model development). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, AWS Redshift, and Other. Education includes Master of Science, Shivaji University (2022) and Bachelor of Science, Mahavir College (2022). AI-training focus includes data types such as Document and labeling workflows including Data Collection, Computer Programming, and Coding.

Labeling Experience

AIT Global India, Senior Data Scientist 1. (LLM-integrated training/evaluation data pipelines)

OtherDocumentData Collection

Built LLM-integrated API services and automated pipelines to extract insights from financial reports using Python, OCR, and SQL. Developed generative AI modules for financial commentary, including summarisation and sentiment analysis. Collaborated on risk scoring models with AI-augmented forecasting and designed APIs supporting portfolio intelligence dashboards. • Used OCR and SQL-based extraction from financial documents to produce structured insights. • Implemented generative AI workflows for summarisation and sentiment analysis of financial text. • Supported AI risk scoring and forecasting model development with prepared datasets. • Designed and deployed APIs to serve portfolio intelligence dashboards from model outputs.

2023 - Present

Futurense Technologies, AI Data Specialist (data preparation for AI modeling)

OtherDocumentData Collection

Built data extraction and transformation pipelines that enriched datasets for pricing and market trend analysis, supporting downstream model development and evaluation. Created automated dashboards for decision-making by engineering datasets from real-time web-scraped sources. This work involved preparing labeled/structured inputs via OCR and structured queries to support AI-ready datasets. • Extracted real-time data via web scraping to enrich pricing and market trend datasets. • Created dashboards using Python, Power BI, and SQL based on transformed datasets. • Automated data flow with ETL and transformation procedures for analytics readiness. • Supported data preparation for modeling and analysis through structured ETL.

2023 - 2023

Systech Solution, Software Engineer (Data Engineer) (AI training data pipeline enablement)

Document

Designed and deployed ETL frameworks for large-scale trading datasets to support real-time analytics and forecasting. Implemented pipeline logic that enabled feature engineering and model optimization workflows, which required prepared training datasets. Enabled monitoring through real-time updates to analytics dashboards and forecasting outputs. • Built ETL pipelines for large-scale trading datasets using DBT and Airflow. • Enabled real-time updates for analytics dashboards used for monitoring asset performance. • Performed time series forecasting, feature engineering, and model optimization support. • Supported experimentation via A/B testing for analytics and model changes.

2022 - 2023

Varilligence Technologies, Data Scientist (AI training / model development)

Don't discloseDocumentData Collection

Conducted R&D for machine learning models for stock price movement prediction using PySpark and financial indicators, supporting data validation and experimentation workflows for quant strategies. Worked with sentiment variables and historical equities datasets to generate and evaluate trading signals in simulation environments. Contributed to model validation and A/B testing cycles that relied on prepared training data and structured inputs. • Built alpha-generating trading signals using historical equities, macro indicators, and sentiment-derived variables. • Performed model validation and A/B testing for quant strategies in simulation environments. • Prepared and used structured model inputs derived from time-series and financial features. • Supported iterative experimentation to improve model performance and reliability.

2020 - 2022

Education

Work History