Senior Data Content Specialist at MILLENNIUM CAPITAL MANAGEMENT SINGAPORE PTE LTD

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

23 May, 26

Salary

0.0

Posted On

22 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Onboarding, Data Curation, ETL Pipelines, Data Structuring, Data Enrichment, Metadata, Compliance, Data Quality Assurance, Financial Content, Portfolio Management, Risk Management, AI Enablement, RAG, NLP, Python, SQL

Industry

Investment Management

Description

Senior Data Content Specialist About the Role Millennium, founded in 1989, is a global alternative investment management firm pursuing diverse investment strategies across industry sectors, asset classes, and geographies. Our primary investment areas include Fundamental Equity, Equity Arbitrage, Fixed Income, Commodities, and Quantitative Strategies. We tackle complex problems at the intersection of computer science, finance, and mathematics, rapidly applying innovations to real-world scenarios. We are seeking a Senior Data Content Specialist to lead the onboarding, curation, and processing of unstructured data for investment management decisions across all asset classes. This role involves transforming diverse content sources—broker research, company filings, earnings reports, central bank press releases, news, podcasts, and multimedia—into structured, enriched, and AI-ready datasets. The ideal candidate will collaborate with portfolio managers, risk managers, and engineering/AI teams to deliver high-quality, actionable data while ensuring compliance, data quality, and access controls. Key Responsibilities Content Onboarding and Curation Identify, onboard, and manage new data sources (e.g., broker research, company filings, multimedia content). Organize and curate unstructured data for relevance, completeness, and usability. Ensure access controls align with licensing agreements and compliance requirements. Data Processing and Structuring Design and implement ETL pipelines to ingest, clean, structure, and enrich unstructured data. Extract structured information from tables, graphs, and multimedia content to make data AI-ready. Enrich data with metadata, annotations, and contextual information (e.g., tagging ticker symbols, sentiment analysis, knowledge graphs). Cross-Functional Collaboration Work closely with portfolio managers, risk managers, and business users to understand data needs. Partner with engineering and AI teams to ensure content is optimized for end-use. Maintain documentation of workflows, processes, and standards. Collaborate with Legal & Compliance to ensure adherence to regulations. AI Enablement Prepare data for advanced AI techniques like Retrieval-Augmented Generation (RAG) and machine learning models. Assist AI teams in understanding document structures and nuances. Identify key features and patterns in unstructured data to improve AI model performance. Quality Assurance Implement processes to monitor and ensure data quality, accuracy, and completeness. Resolve issues in data ingestion, processing, and enrichment workflows. Define and track metrics for data usability and quality. Required Skills and Qualifications Deep understanding of financial content (e.g., broker research, company filings, earnings reports, central bank press releases, multimedia formats). At least 5 years of relevant markets experience, preferably in a data discovery/acquisition role. Strong knowledge of financial concepts and experience in financial services or investment management. Familiarity with financial data platforms (e.g., Bloomberg, FactSet, Refinitiv). Proficiency in working with structured and unstructured data. Experience with data quality assurance processes and metrics. Understanding of content licensing agreements, compliance requirements, and access controls. Excellent communication skills to bridge business requirements and technical solutions. Preferred Skills Experience with ETL pipelines and data processing techniques. Familiarity with AI concepts like Retrieval-Augmented Generation (RAG), NLP, and machine learning workflows. Knowledge of tools for data extraction and enrichment (e.g., Python, SQL, Pandas, NLP libraries). Ability to extract structured information from tables, graphs, and multimedia content. Proven ability to work cross-functionally with portfolio managers, engineers, and AI researchers.

Responsibilities

The specialist will lead the onboarding, curation, and processing of unstructured data from diverse sources like research, filings, and multimedia into structured, AI-ready datasets for investment decisions. Key tasks include designing ETL pipelines, enriching data with metadata, and ensuring data quality and compliance across asset classes.