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No Experience Data Science Mentor Jobs in Rutherford, NJ

Tatari

Data Science Analyst

New York, NY · On-site

$70K - $85K/yr

... like experience, enabling businesses of any size to advertise on TV. That approach has earned ... We're looking for a Data Science Analyst to join the Data Science team. In this role, you will work ...

Tatari

Data Science Analyst

New York, NY

$70K - $85K/yr

... like experience, enabling businesses of any size to advertise on TV. That approach has earned ... We're looking for a Data Science Analyst to join the Data Science team. In this role, you will work ...

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ · On-site

$132K - $264K/yr

Mentor team members on coding, modeling, visualization, and analytical storytelling. * Communicate ... Strong Python coding skills, with experience building production-quality data science solutions.

Smarsh

Lead Data Scientist

New York, NY · On-site +1

The role will involve working with other Senior Data Scientists and mentoring Associate Data ... Experience with one or more data science and machine/deep learning frameworks and tooling ...

Smarsh

Lead Data Scientist

New York, NY · On-site

The role will involve working with other Senior Data Scientists and mentoring Associate Data ... Experience with one or more data science and machine/deep learning frameworks and tooling ...

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All JobsData Science Mentor JobsNo Experience Data Science Mentor JobsNo Experience Data Science Mentor Jobs in Rutherford, NJ

No Experience Data Science Mentor information

See Rutherford, NJ salary details

$38.2K

$125.1K

$200.3K

How much do no experience data science mentor jobs pay per year?

As of Jul 18, 2026, the average yearly pay for no experience data science mentor in Rutherford, NJ is $125,124.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $138,600.00 per year, depending on experience, location, and employer.

Is it possible to get a data science job with no experience?

Entry-level data science roles often accept candidates with little to no professional experience if they have relevant skills such as programming in Python or R, knowledge of statistics, and familiarity with data analysis tools. Building a portfolio through projects, certifications, and internships can improve chances of securing such positions. However, most employers prefer candidates who demonstrate practical skills and a strong understanding of data science concepts.

Is 40 too late for data science?

The No Experience Data Science Mentor role indicates that individuals can start learning data science at any age, including 40. Success depends on dedication, acquiring relevant skills like programming and statistics, and building a portfolio through projects or certifications; age is not a barrier to entering the field.

How to find a mentor for data science?

A No Experience Data Science Mentor can be found through online platforms like LinkedIn, industry meetups, or data science communities where experienced professionals offer guidance. Networking, participating in data science bootcamps, and joining relevant forums can also help connect with mentors who can provide advice on skills, tools, and career development.

What is the difference between No Experience Data Science Mentor vs Data Science Intern?

AspectNo Experience Data Science MentorData Science Intern
Required CredentialsNone or basic understanding, often self-taught or online coursesTypically pursuing or completed relevant coursework or degree
Work EnvironmentOnline or in-person mentorship programs, flexible hoursCompany or organization, structured internship program
Employer & Industry UsageEducational platforms, community programs, startupsTech companies, consulting firms, research labs
Search & Comparison IntentUnderstanding entry-level or mentorship roles without experienceGaining practical experience as a beginner in data science

In summary, a No Experience Data Science Mentor typically offers guidance and support to beginners without requiring prior experience, often through online platforms. In contrast, a Data Science Intern is a beginner-level role within a company, providing hands-on experience in real projects. Both roles are suitable for those starting their data science journey but differ mainly in setting and expectations.

What is the 80 20 rule in data science?

The 80/20 rule in data science suggests that roughly 80% of results come from 20% of efforts or features. Data scientists often focus on identifying the most impactful variables or tasks to optimize model performance and efficiency.
What job categories do people searching No Experience Data Science Mentor jobs in Rutherford, NJ look for? The top searched job categories for No Experience Data Science Mentor jobs in Rutherford, NJ are:
What cities near Rutherford, NJ are hiring for No Experience Data Science Mentor jobs? Cities near Rutherford, NJ with the most No Experience Data Science Mentor job openings:
No Experience Data Science Mentor jobs near you

Research Scientist, Foundational Data Science

Prior Labs

New York, NY • On-site

Full-time

Posted 15 days ago

Job description

Who we are
Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched. Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them.
Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it.
Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical-trial decisions with BostonGene.
The hardest work is ahead. We're scaling tabular foundation models to millions of rows, thousands of features, real-time inference, and entirely new data modalities, while building the infrastructure to run them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level.
Our team. We're a small, highly selective team of 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar.
What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join.
What you'll do
This role is foundational data science: building the foundations of tabular foundation models so a single model can solve data-science problems across the board. Roughly half the work is inventing new frontier tools for TFMs, and half is building the dataset and benchmark bedrock they stand on.
  • Invent and build the frontier tools that extend TabPFN, including its thinking, scaling, and agentic capabilities, and the new methods that let one model generalize across the full landscape of data-science problems. This is the most open-ended part of the work and grows over time.
  • Set the research direction by deciding which model capabilities and benchmarks are worth pursuing, choosing what is worth solving rather than optimizing a score someone else set.
  • Bring in external research and real customer needs to shape new model and tooling directions, and publish frontier results that move the field forward.
  • Build trustworthy benchmarks from the structured data behind real, high-impact problems, so the team optimizes for real-world performance rather than one leaderboard.
  • Faithfully implement the baselines and competitor models that set the gold standard of applied data science, giving the team a read on where TabPFN leads and where there is room to improve.
  • Build an automated, agentic pipeline with a human in the loop so this data and benchmark foundation scales to far larger volumes without losing rigor, itself a genuinely new tool.

What we're looking for
  • You have solved data-science problems across many domains and datasets to a high standard, optimizing for strong performance across a whole suite of tasks rather than the single best score on one.
  • You work undogmatically across the ML toolbox, including getting strong results with gradient-boosted trees (such as XGBoost) and not only with deep learning.
  • You understand the common categories of dataset defects (leakage, label noise, distribution shift, duplication, mislabeled targets, and similar) and why each corrupts a training or benchmark signal.
  • You are energized by foundational work, valuing the dataset and benchmark bedrock as much as the frontier tooling, and you have taken on hard problems others passed over.
  • You thrive as a senior individual contributor in an ambiguous, early-stage, low-process environment. You are opinionated on best practice in Data Science and can make good judgement calls on approaches to complex problems.

Nice to have
  • Experience building or extending evaluation harnesses, benchmark suites, or experiment frameworks that others rely on.
  • Experience building LLM- or agent-assisted pipelines with a human in the loop to scale a previously manual workflow.
  • Experience acting as the link between external research or customer needs and an internal model or product roadmap.
  • Prior work on tabular, structured-data, or foundation-model problems, or helping shape an emerging research subfield through community work.

Life at Prior Labs
We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with.
We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you.
We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together.
Our Commitments
We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box."
We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.
We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.

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