Staff Data Scientist

zipcolimited; United States; Remote; USD 192,000 - 240,000; Full time

Benefits offered by this job

Remote-first within US

Job summary

zipcolimited is leading the analytics strategy for a smoothing product initiative in the financial services space, guiding development of cash advances and rent advances. The role covers zero-to-one problems to scaled adoption, with ownership of measurement, modeling foundations, and risk trade-offs that shape product strategy and sustainable growth.

As a senior analytics partner, you will architect ML/AI solutions on bank transaction data, explore questions that surface opportunities, and

Qualifications

• PhD or equivalent in a quantitative field; advanced degree preferred.
• 7+ years data science, with 4+ years in financial services.
• Strong problem-solving and independent work ability.
• Experience with cashflow data sources such as Plaid, MX, or Finicity.
• Proficiency in Python and SQL; Spark/PySpark is a plus.
• Experience with neural networks, LLMs, and generative AI.

Responsibilities

• Architect, build, and scale ML/AI solutions using bank transaction data.
• Explore open-ended questions to surface new product opportunities.
• Rapid experimentation and delivery of proofs of concept to production.
• Establish evaluation frameworks and decision criteria for scaling or stopping.
• Provide technical direction to other data scientists and own outcomes.

Skills

Python

SQL

Spark/PySpark

LLMs

Neural networks

Statistics

Problem solving

Education

PhD in quantitative field

Tools

Spark/PySpark

Python

SQL

LLMs

Job description

Role overview

Lead the analytical strategy for a smoothing product initiative, acting as the senior analytics partner shaping the development and growth of new financial offerings such as cash advances and rent advances. The work spans ambiguous zero-to-one problems through scaled adoption, with ownership of measurement, modeling foundations, and risk trade-offs that guide product strategy and sustainable expansion.

Responsibilities

• Architect, build, and scale new machine learning and AI solutions using bank transaction data to unlock new product capabilities.
• Explore open-ended questions across bank transaction data and other sources to surface new product opportunities.
• Rapidly experiment, iterate, and deliver proofs of concept that can be tested and refined in production.
• Establish clear evaluation frameworks and decision criteria for what should scale, change, or stop.
• Provide technical direction to other data scientists on complex workstreams while retaining accountability for outcomes.
• Design tests that produce the data needed to build future model iterations.
• Apply AI across the stack, from productivity enhancements to custom neural network and LLM solutions.
• Build models that balance risk mitigation with profitable growth, partnering closely with engineering teams to ensure scalable, stable production systems.

Requirements

• Advanced degree (PhD preferred) in a quantitative field such as Mathematics, Physics, Social Science, or Computer Science.
• 7+ years of data science experience (may include PhD work) with 4+ years specifically in financial services.
• Self-starter mindset with strong problem-solving and research skills; comfortable driving work independently.
• Solid grasp of probability, statistics, and applied research methods.
• Experience working with cashflow data sources such as Plaid, MX, or Finicity.
• Proficiency in Python and SQL; hands-on Spark/PySpark experience is a plus.
• Experience with neural networks, LLMs, and generative AI.

  Nice to have

  • Background in credit or fraud risk modeling.

  Benefits and work setup

  • Remote-first opportunity for US-based employees with the option to work in person from a Manhattan office.
  • Annual base pay range of $192,000 to $240,000, plus potential bonus, commission, and benefits including medical and financial offerings.
  • Visa sponsorship is not available for this position.