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Data Scientist

Job in New York, New York County, New York, 10261, USA
Listing for: Xcede
Full Time position
Listed on 2026-02-15
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist, Machine Learning/ ML Engineer, Data Science Manager
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Location: New York

Senior Data Scientist with deep experience in marketing analytics, product data science, machine learning, and growth strategy to lead our marketing data function and build product-facing data capabilities at a fast-growing Series A startup.

This is a high-impact role for a hands‑on data scientist who combines strong technical expertise in statistics, machine learning, and coding with strategic thinking. You will operate in a fast‑paced, ambiguous environment and play a critical role in shaping product direction, improving customer outcomes, and driving measurable growth.

What you’ll do
  • Own and scale the marketing data science function to support aggressive growth targets.
  • Build and enhance product-facing data features that deliver valuable financial insights to customers.
  • Conduct deep‑dive analyses for risk modeling and underwriting, particularly for customer loans.
  • Work with large, messy datasets to uncover actionable insights and opportunities.
  • Partner with product, marketing, and leadership to drive strategy and inform product decisions.
  • Develop and deploy statistical and machine learning models to support key business initiatives.
What we’re looking for
  • 5+ years of experience in data science, marketing analytics, product analytics, or a related field.
  • Building 0 to 1 data products or services.
  • Analyzing financial markets or consumer research
  • Experience at a fintech, e‑commerce, lending, or D2C business
  • Strong foundation in statistics, machine learning, and experimental design.
  • Advanced proficiency in SQL and Python (R is a plus).
  • Experience working with large-scale datasets and modern data stacks.
  • Ability to translate complex data into clear, strategic recommendations.
  • Comfortable operating in a fast‑paced, ambiguous startup environment.
  • SQL
  • Python
  • R
  • Big data tools and distributed data systems
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