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Temporary Data Scientist Machine Learning Jobs in New York

Nelo Mobile

Data Scientist (Machine Learning)

New York, NY · On-site

$180K - $230K/yr

Move fast, learn fast: hundreds of experiments run monthly; rigorous experimentation culture Why this Role is Different Most Data Science roles currently on the market are focused on optimizing ad ...

Nestle

Senior Data Scientist

Bridgewater, NJ · On-site

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 ...

NYULMC

Data Scientist & Engineer

New York, NY · On-site

Design and build data solutions using state-of-the-art machine learning and informatics methods ... Contribute to data science best practices and mentor team members to elevate the technical ...

Forhyre

Data Scientist

New York, NY

We are looking for a Data Scientist to analyze large amounts of raw information to find patterns ... We also want to see a passion for machine-learning and research. Your goal will be to help our ...

BMW Group

Lead Data Scientist

Woodcliff Lake, NJ · On-site

$181K - $201K/yr

Lead Data Scientist Posting Start Date: 7/20/26 Job Location (Long): Woodcliff Lake, New Jersey ... machine learning methods, and recommend improvements; work with large data sets from inside and ...

Forhyre

Data Scientist

New York, NY

We are looking for a Data Scientist to analyze large amounts of raw information to find patterns ... We also want to see a passion for machine-learning and research. Your goal will be to help our ...

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Temporary Data Scientist Machine Learning information

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

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

What does a Temporary Data Scientist specializing in Machine Learning do?

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

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

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary Data Scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.
What are the most commonly searched types of Data Scientist Machine Learning jobs in New York? The most popular types of Data Scientist Machine Learning jobs in New York are:
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Temporary Data Scientist Machine Learning jobs near you
Infographic showing various Temporary Data Scientist Machine Learning job openings in New York as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Data Scientist (Machine Learning)

Nelo Mobile

New York, NY • On-site

$180K - $230K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 10 days ago

Job description

About Nelo
Nelo is a leading fintech in Mexico reimagining credit to expand consumer buying power. Founded in 2019 by former Uber international growth team leads Kyle Miller and Stephen Hebson, Nelo offers credit cards, BNPL loans, bill payments, and its own marketplace - all under one app - designed from first principles for a mobile-first, real-time payments world.
With $100M in annual revenue, $1B in annualized GMV, and $140M+ raised in equity and debt, Nelo is one of the most capital-efficient consumer fintech companies in Latin America. The company is profitable - positive operating profit in both 2024 and 2025. It runs lean and technical: 60 employees, 8 engineers, with AI driving underwriting, collections, and operations.
  • HQ: Mexico City, Mexico; commercial and people operations in New York City
  • Founded: 2019
  • Team size: 60 FTEs
  • Open roles: 10+
  • Website: nelo.mx
  • Stage: Post-Series A; $100M credit facility (Victory Park Capital, 2022); profitable
  • Benefits: 401k, open PTO, medical, dental, vision, STD, LTD, fertility

Company values:
  • Be your best selves: ambition, hard work, intellectual curiosity
  • Everyone is an owner: all employees receive equity including customer support; 10-year option expiration; equity refreshers
  • Customer first: product decisions grounded in customer benefit
  • Open and honest communication: quarterly financials and board presentations shared company-wide; low tolerance for bureaucracy or gossip
  • Move fast, learn fast: hundreds of experiments run monthly; rigorous experimentation culture
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.

Apply