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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst - **Company:** TOWER RESEARCH CAPITAL, LLC - **Location:** New York, NY, United States - **Salary:** $150,000.0 - $180,000.0 - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, BigQuery, Code Review, Command Prompt, Information Engineering, Linux, Python (Programming Language), Machine Learning, Named Entity Recognition, SQL Databases, Unstructured Data, Workflow Management Systems, Google Cloud, Large Language Models, Snowflake, Git, Information Technology, Software Version Control, Data Pipelines, Databricks - **Published:** September 24, 2026 - **Apply:** https://www.dice.com/job-detail/70a12f92-9ee1-4ad1-ba0c-f9fea307fd52 ## About the Role * Master's or PhD in Computer Science, Engineering, Mathematics, Statistics, Physics, Economics, or a related quantitative discipline * Strong proficiency in Python and SQL. Evidence of this through coursework, thesis/research code, internships, or personal projects * Internship or research experience involving data engineering, quantitative research, market data, or financial datasets * Practical experience with LLMs for unstructured data processing (document/entity extraction, classification, summarization) and with evaluation harnesses or human-in-the-loop review * Coursework or project experience with workflow tools (Airflow, Dagster), cloud platforms (AWS/Google Cloud Platform), or warehouses (Snowflake, BigQuery, Databricks). * Exposure to financial instruments, market microstructure, or vendor datasets (Bloomberg, S&P, LSEG) * Prior work in a collaborative research lab or production software setting * Hands-on experience with machine learning or applied AI through coursework, thesis/research, internships, or projects * Comfortable with at least one of: classical ML (scikit-learn or similar), NLP, or LLMs (prompting, evaluation, structured extraction) * Demonstrated ability to work with messy, large, or imperfect datasets: cleaning, validating, summarizing, and drawing defensible conclusions * Familiarity with Linux or Windows command line, version control (Git), and basic software engineering hygiene (testing, documentation, code review) * Clear written and verbal communication * Ability to explain technical and AI-related findings to both engineers and non-technical stakeholders, including limitations and failure modes * Strong problem-solving skills, intellectual curiosity, and a bias toward getting details right * Comfortable working in a fast-paced, high-accountability environment ## Description * Contributing to batch and real-time data pipelines that ingest, cleanse, and normalize structured and unstructured sources (market data, vendor feeds, web scrapes, alternative data) * Writing and maintaining Python and SQL under the guidance of senior engineers * Applying AI/ML methods to unstructured and semi-structured sources * Prototyping prompts, models, and evaluation sets; helping productionize approaches that meet the firm's accuracy bar * Implementing and running validation checks, anomaly detection, and reconciliation logic * Using statistical and ML-based methods to flag outliers and data breaks; investigate root causes and help prevent recurrence * Assisting with onboarding new datasets: review vendor specs and sample files, map fields to internal models, and help integrate APIs under senior oversight * Evaluating where LLMs or classical ML can accelerate mapping, documentation, and QA * Using modern data tooling to monitor jobs, improve reliability, and document processes * Building fluency in financial instruments, market data conventions, production engineering practices, and responsible use of AI on sensitive financial data * Taking ownership of well-scoped datasets and processes as you ramp ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)