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Volunteer Data Scientist Machine Learning Jobs (NOW HIRING)

Why this Role is Different Most Data Science roles currently on the market are focused on optimizing ad clicks or slightly improving recommendation engines. This isn't that. At Nelo, your models are ...

Data Scientist / Machine Learning Engineer, GenAI We are not accepting C2C or 1099 arrangements. Location: Charlotte, NC or Irving, TX Work Model: Hybrid (3 days onsite per week) Duration: 12-month ...

Analyze large and complex datasets to identify patterns, build statistical and machine learning ... Integrate the latest data science innovations into product solutions, enhancing data, analytical ...

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Volunteer Data Scientist Machine Learning information

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$37.5K

$122.7K

$196.5K

How much do volunteer data scientist machine learning jobs pay per year?

As of Jul 11, 2026, the average yearly pay for volunteer data scientist machine learning in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Volunteer Data Scientist (Machine Learning), and why are they important?

To thrive as a Volunteer Data Scientist (Machine Learning), you need proficiency in statistics, data analysis, programming (Python or R), and a foundational understanding of machine learning algorithms, often supported by a relevant degree or online certifications. Familiarity with tools like scikit-learn, TensorFlow, Jupyter Notebooks, and data visualization platforms is typically required. Strong problem-solving abilities, teamwork, and effective communication are crucial soft skills for translating complex data insights to non-technical stakeholders. These skills and qualities are essential to effectively contribute value, support decision-making, and drive impact in resource-limited volunteer environments.

How do Volunteer Data Scientist Machine Learning roles typically collaborate with other team members or departments?

As a Volunteer Data Scientist specializing in Machine Learning, you will often work closely with cross-functional teams such as project managers, software engineers, and subject matter experts. Effective collaboration is essential, as you may need to clarify project goals, source and preprocess data, or translate complex findings for non-technical stakeholders. Regular meetings and open communication help ensure that your machine learning solutions are aligned with the organization's mission and that your insights are actionable. This collaborative environment provides valuable experience working in diverse teams and often leads to impactful, real-world applications of your technical skills.

What is the difference between Volunteer Data Scientist Machine Learning vs Volunteer Data Analyst?

AspectVolunteer Data Scientist Machine LearningVolunteer Data Analyst
Required CredentialsKnowledge of machine learning algorithms, programming skills (Python, R), basic statisticsProficiency in data visualization, basic statistics, Excel, SQL
Work EnvironmentCollaborative projects, research-focused, often remote or nonprofit settingsData reporting, dashboard creation, data cleaning in nonprofit or community projects
Employer & Industry UsageTech nonprofits, research institutions, startupsCharities, educational organizations, community initiatives

Volunteer Data Scientist Machine Learning focuses on developing predictive models and advanced analytics, requiring programming and machine learning expertise. Volunteer Data Analyst emphasizes data interpretation, visualization, and reporting. Both roles support nonprofits but differ in technical complexity and focus areas.

What does a Volunteer Data Scientist in Machine Learning do?

A Volunteer Data Scientist in Machine Learning applies data analysis and machine learning techniques to help organizations solve problems, often for nonprofits or community projects. They may work on tasks such as cleaning and analyzing datasets, building predictive models, or creating data visualizations. Their work supports impactful decision-making and can help organizations operate more efficiently or achieve specific social goals. Volunteers often collaborate with teams to define project objectives and deliver actionable insights using their technical expertise.
What cities are hiring for Volunteer Data Scientist Machine Learning jobs? Cities with the most Volunteer Data Scientist Machine Learning job openings:
What are the most commonly searched types of Data Scientist Machine Learning jobs? The most popular types of Data Scientist Machine Learning jobs are:
What states have the most Volunteer Data Scientist Machine Learning jobs? States with the most job openings for Volunteer Data Scientist Machine Learning jobs include:

Data Scientist (Machine Learning)

Nelo Mobile

New York, NY • On-site

$180K - $230K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 21 days ago


Job description

About Nelo
Nelo is a leading consumer fintech and e-commerce platform in Mexico, with >$500MM in annualized GMV and >$70MM in annualized revenue. Our mission is to increase the buying power of consumers in Latin America, and we are doing so by building a modern alternative to credit cards.
Nelo has raised over $40M of venture capital from investors including Homebrew, Two Sigma Ventures and Susa Ventures. Nelo has additionally raised a $100M asset credit facility from Victory Park Capital.
Our lean team includes experienced leaders from top technology companies including Uber, Amazon, Rappi, and DiDi. We pride ourselves on our velocity, intellectual rigor, and efficiency. Nelo has offices in Mexico City and New York City.
Why this Role is Different
Most Data Science roles currently on the market are focused on optimizing ad clicks or slightly improving recommendation engines.
This isn't that.
At Nelo, your models are the product. You are building the decision engine that determines who gets access to credit in an emerging market. This involves high-stakes constrained optimization problems where "good enough" mathematics will result in direct financial loss.
We are looking for the type of person who is frustrated by the "black box" approach of modern libraries and actually understands the statistical theory and causality behind the code. If you want to apply academic-level rigor to a P&L that is scaling rapidly, this is your seat.
What You'll Do:
  • Solve the "Why," not just the "What": You will design and deploy causal inference models to drive our underwriting and portfolio management strategies. Correlation isn't enough when you're managing risk.
  • Build the Core Engine: You will create and refine the algorithms for credit pricing, personalization, and ranking. Your code will directly impact the wallet of the consumer and the margin of the company.
  • Own the Infrastructure: You won't just hand off a Jupyter notebook to an engineer. You will lead ML infrastructure projects, ensuring observability and operational excellence for the models you build.
Who You Are:
  • You have deep theoretical roots. We are explicitly looking for candidates with a strong academic background (PhD preferred) who understand the first principles of classification, forecasting, and optimization.
  • You are a builder, not just a researcher. While you love the theory, you have at least 5 years of experience applying it in a production environment. You write production-grade Python and SQL.
  • You value velocity. You understand that a perfect model shipped next year is worth less than a great model shipped next week. You can balance intellectual rigor with the need to execute.
  • You are happy in NYC. This is an in-office role. We believe the hardest problems are solved when smart people are in the same room with a whiteboard.
What's on the Table
  • Significant Equity (You're building the company, you should own it).
  • 100% medical, dental & vision insurance coverage for you (50% for dependents).
  • Unlimited PTO (that we actually expect you to take).
  • 401(k).
  • Extended maternity and paternity leave.
  • Relocation support and Sabbatical program.
About the Process
We know you're busy, so we don't do 8-stage interviews.
  1. Quick chat with the Hiring Manager to align on expectations.
  2. A business case/technical assessment (relevant to the actual job).
  3. Onsite interview in NYC to meet the team.
  4. Offer.
This isn't a job for someone who wants to hide in the back office; it's for someone who wants their math to move the market.