Hiring: Senior Data Analyst Company: Tower Research Capital Location: New York, United States Job Posted Time: 2026-09-16 21:39:31 Employment Type: Hybrid Target Skills & Keywords : AWS, Airflow, BigQuery, C++, Confluence, Dagster, Data Pipeline, Data Warehouse, Databricks, Derivatives, Equities, FPGA, Fine-tuning, GCP, Hugging Face, Java, Jira, Kafka, LLM, Linux, Machine Learning, NLP, NumPy, Pandas, PyTorch, Python, Risk Management, Rust, S3, SQL, Snowflake, Spark, scikit-learn About the job Experience: •25+ year track record of innovation and a reputation for discovering unique market opportunities. •3+ years of professional experience in data engineering, preferably within the financial industry (Hedge Fund, Asset Manager, or FinTech) Required Skills: •Designing, building, and maintaining scalable batch and real-time data pipelines to ingest, cleanse, and normalize data from a wide variety of structured and unstructured sources (market data, web scrapes, vendors, alternative data) •Designing and productionizing AI/ML workflows for unstructured and semi-structured data, including document/entity extraction, classification, vendor-file parsing, news and filings processing, and alternative-data onboarding •Owning prompt/model selection, evaluation harnesses, human-in-the-loop review, and monitoring so AI-assisted feeds meet the firm’s accuracy and latency standards •Owning core investment data domains, designing and evolving data models for Security Masters, Corporate Actions, and Referential datasets across various asset classes (Equities, Futures, FX, Derivatives) •Evaluating and implementing modern data tooling (SQL, Kafka, Airflow, Cloud) to improve the speed, reliability, and observability of the data ecosystem •Implementing robust validation checks, anomaly detection, and reconciliation logic to ensure "zero-error" data delivery to trading systems •Applying statistical and ML-based methods to detect outliers, drift, and silent data breaks •Partnering directly with Data Scientists and Quants to understand their research needs, prototype data extraction methods (including AI-enabled approaches), and operationalize research signals into production-grade feeds Qualifications: •Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline •Understanding of financial instruments, financial datasets from Bloomberg, S&P, LSEG •Proficiency in C++, Java, or Rust is a strong plus •Prior work applying AI to financial documents, corporate actions, or alternative data •Programming Mastery: Expert-level proficiency in Python (including Pandas, NumPy, and async frameworks) and advanced SQL (complex queries, window functions, performance tuning) •Hands-on production experience applying AI or ML to data problems—such as NLP, information extraction, classification, or LLM-based processing of unstructured data •Demonstrated capacity to evaluate model quality (precision/recall, error analysis, gold-set design), manage failure modes, and ship reliable pipelines rather than one-off prototypes •Operational familiarity with common AI libraries and APIs (e.g., scikit-learn, PyTorch, Hugging Face, and/or LLM APIs) •Strong hands-on experience with workflow orchestration tools such as Airflow, Dagster, or similar •Proven experience building data platforms on AWS / GCP and modern data warehouses (e.g., Snowflake, BigQuery, DataBricks) Compensation: •Generous paid time off policies Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!