Senior Data Scientist II
New
Z
ZipFinancial services
Remote-first opportunity for US-based employees with the option to work in-person out of our Manhattan officeFull-TimeSenior
SalaryThe annual base Pay Range for this position is $162,000 - $203,000. This range reflects our US national compensation band (USN). Additional premium percentages may apply based on our tiered premium strategy.
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Job Details
- Experience
- 5+ years of experience in Data Science (can include PhD work); 2+ years of experience working with data relevant to Financial Services
- Required Skills
- PythonSQLData scienceGenerative AI
Requirements
- Hold an advanced degree in a quantitative field such as Mathematics, Physics, Social Science, or Computer Science; PhD preferred.
- Have 5+ years of experience in Data Science, which can include PhD work.
- Have 2+ years of experience working with data relevant to Financial Services.
- Bring a solid grasp of probability, statistics, and applied research methods.
- Demonstrate proficiency in Python and SQL.
- Have experience with Neural Networks, LLMs, and GenAI.
- Bring self-starter mentality, problem-solving and research skills, and the ability to work independently.
- Be familiar with version control systems such as Git.
- Experience working with cashflow data, such as Plaid, MX, or Finicity, is highly preferred.
- Hands-on experience with Spark/PySpark is a plus.
- Experience with credit and/or fraud risk is a plus.
Responsibilities
- Design, build, evaluate, and iterate on AI/ML models using bank transaction data.
- Rapidly experiment with solutions and develop realistic proofs of concept for testing and refinement.
- Assist with test designs that produce data for future model versions.
- Balance short-term business outcomes with longer-term experimental opportunities.
- Use AI to improve model quality, analytical efficiency, or product outcomes.
- Build models that mitigate risk while driving profitable growth.
- Work with Data and Machine Learning Engineering teams on data availability and model scalability.
- Own modeling solutions end-to-end, from development through production monitoring.
- Communicate findings, recommendations, and tradeoffs to business stakeholders.
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