ZipRecruiter

Log In

2

Entry Level Meta Data Science Jobs (NOW HIRING)

Alcon

Senior Associate Data Science

Fort Worth, TX · On-site

As a Senior Associate, Data Science , you will be part of Alcon's Analytics & Intelligence function ... Meta-Heuristics) and Machine Learning techniques, * Strong proficiency in programming languages ...

MDAEdge

Principal Data Science Engineer

$138K - $185K/yr

They are seeking a high-impact principal data science engineer to join an early-stage team with a ... Amazon (strongly preferred), Apple, Twitter, Google, Meta, Microsoft, Netflix, etc. Company : The ...

Outsource

Be Seen First

Full-Time Traveling Data Center Low Voltage Network Specialist

Phoenix, AZ · On-site

$20/hr

Nationwide travel | Meta data center projects Pay: $20/hr + $30/hr overtime Per Diem: $1,400+/month ... Recent IT, computer networking, computer science, engineering, telecommunications, or trade-school ...

AIToolboard

Jobs / Entry-Level Data Scientist - AI Chatbot Development Entry-Level Data Scientist - AI Chatbot ... science. #J-18808-Ljbffr

SynergisticIT

Entry Level Data Scientist

Charleston, SC · On-site

... * Entry-level software programmers * Java Full stack developers * Python/Java developers * Data ... Recent computer science/engineering/mathematics/statistics or science graduates or people looking ...

SynergisticIT

Entry Level Data Scientist

Charleston, SC · On-site

... * Entry-level software programmers * Java Full stack developers * Python/Java developers * Data ... Recent computer science/engineering/mathematics/statistics or science graduates or people looking ...

previous page

Showing results 41-60

All JobsMeta Data Science JobsEntry Level Meta Data Science Jobs

Entry Level Meta Data Science information

See salary details

$13

$32

$61

How much do entry level meta data science jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for entry level meta data science in the United States is $32.93, according to ZipRecruiter salary data. Most workers in this role earn between $21.15 and $36.78 per hour, depending on experience, location, and employer.

What is the difference between Entry Level Meta Data Science vs Entry Level Data Analyst?

AspectEntry Level Meta Data ScienceEntry Level Data Analyst
Required CredentialsBachelor's in CS, Data Science, or related; familiarity with machine learning and programmingBachelor's in Statistics, Business, or related; proficiency in Excel, SQL, and data visualization
Work EnvironmentTech companies, e-commerce, social media platforms, often collaborative and project-basedBusiness, finance, marketing departments; focus on reporting and insights
Employer & Industry UsageUsed in tech, advertising, and online services for developing models and algorithmsCommon in corporate, retail, and finance sectors for analyzing data trends

Entry Level Meta Data Science roles focus on developing machine learning models and working with large datasets, often requiring programming skills. Entry Level Data Analyst positions emphasize data interpretation, reporting, and visualization to support business decisions. Both roles are entry-level but differ in technical complexity and focus areas.

Does Meta have entry-level positions?

Meta offers entry-level positions in data science, including roles such as Data Scientist I, which typically require a bachelor's degree in a related field and some experience with statistical tools and programming languages like Python or R. These roles often provide training and opportunities to develop skills in machine learning, data analysis, and visualization within a collaborative environment.

How much does an entry-level meta data scientist make?

An entry-level data scientist typically earns between $60,000 and $85,000 annually, depending on location, industry, and company size. Skills in programming, statistical analysis, and tools like Python or R can influence starting salaries for this role.
More about Entry Level Meta Data Science jobs

What cities are hiring for Entry Level Meta Data Science jobs?

Cities with the most Entry Level Meta Data Science job openings:

What are the most commonly searched types of Meta Data Science jobs?

The most popular types of Meta Data Science jobs are:

What are popular job titles related to Entry Level Meta Data Science jobs?

For Entry Level Meta Data Science jobs, the most frequently searched job titles are:

Entry Level Meta Data Science jobs near you
Infographic showing various Entry Level Meta Data Science job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $68,487 per year, or $32.9 per hour.

Research Scientist, Foundational Data Science

New York, NY • On-site

Prior Labs
See more jobs at Prior Labsexternal

Full-time

Re-posted 10 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, until now. What LLMs did for language, we're doing for tables.
We pioneered tabular foundation models: TabPFN v2 was a Nature cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level.
We're a small, highly selective team of 40+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun.
In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.
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
You'll work alongside researchers and builders who hold themselves to a very high bar - in the quality of their work and in how they work with each other. We move fast and still take the time to do things right.
Our teams are based in Berlin, Freiburg, and New York - when you're working on something as hard as TabPFN, being in the same room matters. But great people come from everywhere, and in exceptional cases we're open to remote, which usually means frequent travel to one of our offices. Wherever you're based, the whole company comes together regularly for offsites to build and celebrate together.
Our Commitments
The best products and teams are built by people with a wide range of perspectives and backgrounds. We welcome applications from all identities and walks of life - especially if you've ever felt discouraged by "not checking every box" - and provide 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 - see our Recruiting Data Privacy page

Apply