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Pure Data Jobs (NOW HIRING)

Technical Capabilities This is not a pure data science research role. However, you must be technical enough to work with data, test assumptions, and answer practical modeling questions. Helpful ...

Senior Accountant

Orlando, FL · On-site

$69K - $86K/yr

This is not a pure data-entry or bookkeeping role; the ideal candidate must be comfortable reviewing work, identifying issues, and operating independently in a fast-paced accounting environment.

You don't need to be a pure data scientist or engineer, but you should have strong technical data fluency. Our core stack includes MySQL, Python, Notion, and MS Office / Google Suite. We build ...

Senior Accountant

Orlando, FL · On-site

$69K - $86K/yr

This is not a pure data-entry or bookkeeping role; the ideal candidate must be comfortable reviewing work, identifying issues, and operating independently in a fast-paced accounting environment.

You don't need to be a pure data scientist or engineer, but you should have strong technical data fluency. Our core stack includes MySQL, Python, Notion, and MS Office / Google Suite. We build ...

Senior Accountant

Orlando, FL · On-site +1

$69K - $86K/yr

This is not a pure data-entry or bookkeeping role; the ideal candidate must be comfortable reviewing work, identifying issues, and operating independently in a fast-paced accounting environment.

A pure data engineering or operations role * A staff-augmentation position ABOUT ISHIR ISHIR is a digital innovation and enterprise AI services provider. We work with startups and enterprises to ...

A pure data engineering or operations role * A staff-augmentation position ABOUT ISHIR ISHIR is a digital innovation and enterprise AI services provider. We work with startups and enterprises to ...

A pure data engineering or operations role * A staff-augmentation position ABOUT ISHIR ISHIR is a digital innovation and enterprise AI services provider. We work with startups and enterprises to ...

About Pure Pure is building the future of rare coin and precious metal trading. We believe ... Data-driven and metrics-oriented: you measure progress, optimize efforts, and refine processes.

About Pure Pure is building the future of rare coin and precious metal trading. We believe ... Data-driven and metrics-oriented: you measure progress, optimize efforts, and refine processes.

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Pure Data information

What are some typical challenges faced by professionals working in a Pure Data role?

Professionals in a Pure Data role often encounter challenges such as managing large and complex datasets, ensuring data integrity, and deriving actionable insights from raw information. Team members must also stay up to date with evolving data technologies and adapt to new tools or methodologies as they emerge. Additionally, collaborating with stakeholders who may have varying levels of data literacy can require strong communication and education skills. Overcoming these challenges is essential for delivering high-quality, reliable analyses that drive organizational success.

What is a Pure Data job?

A Pure Data job typically involves working with Pure Data (Pd), an open-source visual programming language used for multimedia and interactive applications. Professionals in this field often develop real-time audio or visual processing systems, interactive installations, or experimental music applications. Common roles include sound designers, multimedia artists, and software developers specializing in computer music and generative art. Skills in digital signal processing, synthesis, and programming are often essential for these positions.

What are the key skills and qualifications needed to thrive in the Pure Data position, and why are they important?

To excel in a Pure Data role, you should have strong proficiency in data analysis, statistical modeling, and a background in mathematics, statistics, computer science, or a related field. Technical expertise with software tools such as SQL, Python, R, and experience with data visualization platforms or cloud-based data storage systems is highly valued. Critical thinking, attention to detail, and effective communication skills are key soft skills for interpreting data and presenting findings. These capabilities enable accurate data-driven decision making and support strategic objectives across various industries.

More about Pure Data jobs
Infographic showing various Pure Data job openings in the United States as of May 2026, with employment types broken down into 7% Full Time, 75% Part Time, and 18% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Contractor

Posted 3 days ago


Job description

Mission
Help CRNCY become the world's best underwriter of credit risk using incomplete, noisy, unstructured, and alternative data.
CRNCY serves customers who are often underbanked, thin-file, or locked out of traditional lending options. Many have real repayment capacity but lack the formal credit history, documentation, or banking footprint that traditional lenders require.
Our challenge is to build a decision framework that helps us identify good borrowers from imperfect information and lend more money to more people with the same or lower risk.
The Challenge
This role is about making better lending decisions under uncertainty.
We want to answer questions such as:
  • How much information is enough to make a good credit decision?
  • Which underwriting requirements create value, and which create unnecessary friction?
  • What risks are worth taking?
  • How do we lend more while losing less?
  • How do we identify creditworthy customers traditional lenders miss?
  • How do we align loan amount, pricing, risk, expected loss, and profitability?
First Mission: First-Time Loan Sizing
Our current underwriting rules have helped control risk and maintain strong recoveries. However, we believe we may be under-lending to strong first-time customers because our rules are still too conservative, conditional, and one-size-fits-all.
Your first mission will be to identify which first-time customers can responsibly support higher offers, recommend better first-loan amount bands, and design controlled tests to validate changes without weakening portfolio discipline.
What You'll Do
  • Improve lending decisions under uncertainty.
  • Evaluate which underwriting rules and requirements reduce risk versus create friction.
  • Identify where CRNCY can safely lend more to strong first-time customers.
  • Quantify trade-offs between approval growth, conversion, risk, expected loss, recoveries, and profitability.
  • Translate data, models, and experiments into practical underwriting decisions.
  • Design controlled tests to validate changes before full rollout.
  • Help CRNCY move toward risk-based loan sizing, pricing, and scalable credit decisioning.

Requirements
The Type of Person We Need
You naturally think in probabilities, trade-offs, and expected value.
You are uncomfortable with rules that exist only because "that's how we've always done it." You instinctively ask:
  • What is the probability of this outcome?
  • What is the cost if it happens?
  • What is the cost of preventing it?
  • Is the risk worth the reward?
  • What is the economically rational decision?

You are not just interested in prediction. You are interested in decision quality.
Ideal Background
The ideal candidate has worked in environments where decisions had to be made under uncertainty using incomplete or imperfect data. A degree in Decision Science, Risk Management, Economics, Statistics would be preferred.
Strong candidates may have experience with:
  • Decision science, risk optimization, lending strategy, or portfolio economics.
  • Customer segmentation, expected value analysis, risk-adjusted returns, or pricing optimization.
  • Credit risk, underwriting analytics, scorecards, probability of default, first-payment default, expected loss, or repayment behavior analysis.
  • Insurance-related risk work such as actuarial pricing, underwriting analytics, risk selection, loss forecasting, claims analytics, fraud detection, or risk-based pricing.
  • Alternative data, behavioral data, unstructured data, or thin-file customer environments.
  • Experimentation, causal inference, A/B testing, champion/challenger testing, Bayesian testing, or Monte Carlo simulation.
  • Using messy internal data to improve real business decisions.
Technical Capabilities
This is not a pure data science research role. However, you must be technical enough to work with data, test assumptions, and answer practical modeling questions.
Helpful capabilities include:
  • SQL and Python.
  • Probability, statistics, segmentation, and predictive modeling.
  • Logistic regression, scorecards, XGBoost, LightGBM, or similar practical models.
  • Cohort analysis, vintage analysis, expected loss, customer lifetime value, and portfolio performance tracking.
  • Backtesting, out-of-time validation, data leakage prevention, and scenario testing.
What This Role Is Not
This is not a general business analyst role, a pure machine learning research role, or a role for someone who needs perfect bureau data, open banking, or fully automated cashflow tools before producing useful insights.
We need someone who can work with imperfect information, think clearly about risk and reward, and help us make economically rational credit decisions.
Benefits
This role offers the opportunity to help change the lives of people who are underbanked, thin-file, or often overlooked by traditional lenders. By building better credit decisioning frameworks, you will help CRNCY identify customers with real repayment capacity and give them access to more appropriate financial products.
You will work on decisions that directly affect loan approvals, first-loan offers, customer experience, repayment performance, and responsible credit access. The work is high-impact, highly visible, and tied to a mission that goes beyond optimization: helping more people access credit fairly while managing risk intelligently.
CRNCY offers a remote working environment, exposure to emerging-market lending, close collaboration with senior leadership, and the opportunity to help build a durable underwriting advantage using alternative data, behavioral data, internal data, and real-world outcomes.