About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, a Confidential Client in Investment Management.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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What You’ll Do

• Build, maintain, and improve Python-based data pipelines to ingest, process, and visualize structured and alternative datasets (e.g., card transactions, POS, vendor feeds, internal data).
• Design ingestion workflows that are reliable, repeatable, observable, and easy to operate — handle schema changes, late-arriving data, vendor restatements, duplicates, and missing values.
• Implement automated data quality checks (completeness, timeliness, consistency) and clear escalation procedures for pipeline failures.
• Transform raw inputs into clean, analysis-ready outputs that map to business metrics and analyst workflows: profiling, normalization, enrichment, deduplication, entity resolution, outlier handling.
• Document dataset lineage, definitions, coverage gaps, limitations, and assumptions so analysts and PMs can use data confidently.
• Collaborate closely with portfolio managers and senior analysts to translate research questions into data requirements and deliver well-documented extracts, dashboards, and ad-hoc analyses.
• Surface explainable anomalies, data-driven alerts, and metric changes with clear technical and business context.
• Prepare and maintain data assets suitable for analytics tooling, LLM/RAG workflows, AI agents, and retrieval-based applications.
• Identify practical AI-enabled use cases that improve analyst efficiency (conversational exploration, automated summaries, data-quality explanations, metric lookup).
• Apply statistical and time-series techniques for KPI forecasting, anomaly detection, trend analysis, and disciplined backtesting when required.
• Communicate findings, caveats, and model assumptions clearly to both technical and non-technical stakeholders.

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What We’re Looking For

Required

• Hands-on experience with Python for data engineering and analysis (pandas, numpy) and data validation techniques.
• Bachelor’s degree in Computer Science, Data Engineering, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
• Solid grounding in statistics, time-series analysis, forecasting, anomaly detection, and backtesting best practices.
• Ability to translate raw data into meaningful business metrics and clearly explain data limitations.
• Familiarity with LLM tooling and interest in applying LLMs/AI to analyst workflows.
• Strong written and verbal communication skills; able to present technical findings and data caveats to analysts and decision-makers.
• Highly organized, self-directed, and comfortable managing multiple datasets and pipelines in a fast-paced environment.

Preferred

• Experience working with alternative data vendors, investment research datasets, or other high-volume third-party financial data sources.
• Proven experience building and operating end-to-end production or business-critical data pipelines.
• Experience preparing datasets for LLMs, retrieval-augmented generation, semantic search, or conversational data exploration.

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Location & Schedule

• Onsite — Midtown Manhattan, New York, NY (fully onsite 5 days/week)
• Employment type: Full-time

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Compensation & Benefits

• Compensation range: $85,000–$100,000 (annual)
• Benefits: full medical & vision coverage, 401(k)
• Where state or local pay-transparency laws apply, the posted range complies with disclosure requirements.

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.