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Meta Data Scientist Jobs in Boston, MA (NOW HIRING)

Meta

Competitive Intelligence Lead

Boston, MA

$177K/yr

As a Competitive Intelligence Lead in Meta's Competitive Intelligence organization, you will operate at the intersection of advanced analytics, data science, and market strategy. You will lead major ...

Meta

Edge Delivery Manager

Boston, MA

$135K/yr

... data-backed approach * Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) About Meta: Meta builds ...

Meta

Software Engineer, Product

Boston, MA · On-site +1

$183K/yr

Meta is seeking talented experienced engineers to join our teams in building cutting-edge products ... Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or ...

NTT DATA, Inc.

AI Security Architect

Boston, MA · On-site

Provide architectural guidance to AI engineers, data scientists, and development teams. * Lead ... Google Gemini * Meta Llam * Mistral * Hugging Face * I Frameworks * LangChain * LangGraph

Orum Therapeutics

Bioanalytical Scientist

Lexington, MA · On-site

$39.50 - $49.75/hr

Orum Therapeutics is seeking a highly motivated and accomplished scientist to lead the ... data, with hands-on experience in PK/PD modeling and simulation, population PK analysis, and meta ...

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All JobsMeta Data Scientist JobsMeta Data Scientist Jobs in Boston, MA

Meta Data Scientist information

See Boston, MA salary details

$40.7K

$133.3K

$213.5K

How much do meta data scientist jobs pay per year?

As of Aug 20, 2026, the average yearly pay for meta data scientist in Boston, MA is $133,327.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $147,700.00 per year, depending on experience, location, and employer.

What is a Meta Data Scientist?

A Meta Data Scientist at Meta (formerly Facebook) uses data analysis, machine learning, and statistical techniques to drive business decisions and improve products. They work with large-scale datasets to extract insights, optimize algorithms, and enhance user experiences. Responsibilities often include A/B testing, predictive modeling, and collaborating with engineers and product teams. The role requires strong programming skills (Python, SQL, R), expertise in statistical analysis, and experience with data visualization. Typically, candidates have backgrounds in computer science, mathematics, or related fields.

What are some typical projects or challenges a Meta Data Scientist might encounter on the job?

As a Meta Data Scientist, you may work on projects ranging from large-scale data infrastructure optimization to advanced predictive modeling and experimentation. Challenges often include handling massive and complex datasets, ensuring data quality, and translating business needs into data-driven solutions. You'll likely collaborate closely with engineers, product managers, and other scientists to deliver impactful insights and scalable tools. This dynamic environment encourages creative problem-solving and offers opportunities for skill advancement and cross-functional learning. The role frequently involves balancing technical execution with clear communication to stakeholders.

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

To thrive as a Meta Data Scientist, you need a strong background in statistics, machine learning, data analytics, and computer science, often supported by an advanced degree in a relevant field. Familiarity with programming languages like Python or R, cloud computing platforms, big data tools such as Spark or Hadoop, and certifications in data science or analytics are highly valuable. Strong collaborative, problem-solving, and communication skills help foster insights and share results effectively across multidisciplinary teams. These skills are essential for building scalable models, generating actionable data-driven insights, and effectively contributing to organizational innovation.

How much do meta data scientists make?

Meta Data Scientists typically earn a median salary ranging from $100,000 to $150,000 annually, depending on experience, location, and education. Senior roles or those with specialized skills in machine learning and data engineering can earn higher compensation, often exceeding $180,000 per year.

How to get a data science job at Meta?

To get a data science job at Meta, candidates should have strong skills in statistics, programming (Python or R), and data analysis, along with experience in machine learning and data visualization. A relevant degree in a quantitative field and a solid portfolio of projects or prior work can improve chances. Familiarity with tools like SQL, Hadoop, or Spark and understanding of the company's products and data infrastructure are also beneficial.

What are the most commonly searched types of Meta Data Scientist jobs in Boston, MA?

The most popular types of Meta Data Scientist jobs in Boston, MA are:

What are popular job titles related to Meta Data Scientist jobs in Boston, MA?

For Meta Data Scientist jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Meta Data Scientist jobs in Boston, MA look for?

The top searched job categories for Meta Data Scientist jobs in Boston, MA are:

Meta Data Scientist jobs near you
Infographic showing various Meta Data Scientist job openings in Boston, MA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $133,327 per year, or $64.1 per hour.

Scientist II / Senior ML Scientist, Data-Efficient Learning for Drug Discovery

Lila Sciences

Cambridge, MA • On-site

Full-time

Medical, Dental, Vision, Life

Posted 17 days ago

Job description

Your Impact at LILA
Lila Sciences is seeking a Machine Learning Scientist, Data-Efficient Learning for Drug Discovery to build models and learning strategies for settings where data is scarce, expensive, and intentionally generated. This role is focused on training useful models from low-quantity but high-quality datasets ranging from as few as tens to low thousands of examples, often in tightly focused areas of chemical space, and deciding what data should be acquired next.
This is an applied scientific ML role in a frontier research area. The work is not a matter of applying standard models out of the box. You will use and develop approaches across active learning, meta-learning, fine-tuning, uncertainty estimation, experimental design, and multimodal modeling to help Lila build closed-loop systems that learn efficiently from targeted data acquisition.
This role connects model training with scientific decision-making: data acquisition plans should be useful to computational chemists evaluating compound priorities, computational biophysicists deciding when simulation is warranted, and cofolding modelers deciding which protein-ligand data would improve structure-aware models.
What You'll Be Building
  • Build ML models that perform well in low-data regimes for drug discovery and molecular optimization.
  • Design data acquisition strategies that identify which compounds, assays, DEL selections, simulations, structural predictions, or experiments should be run next to maximize learning.
  • Develop active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces.
  • Train models on low-quantity, high-quality datasets generated by Lila's experimental, computational, and agentic discovery systems.
  • Build multimodal models that can integrate DEL data, simulation outputs, assay data, protein and structural information, chemical features, literature or text-derived signals, images, and experimental metadata.
  • Partner with experimental, computational, and drug discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible.
  • Evaluate models through learning curves, prospective validation, retrospective benchmarks, uncertainty calibration, and decision-focused metrics.
  • Develop closed-loop learning workflows that continuously update models as new data arrives from experiments, simulations, and automated systems.
  • Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, batch design, or next experiments.
  • Work with platform and agent teams to expose model-driven recommendations as tools for scientists and AI agents.

What You'll Need to Succeed
  • PhD or equivalent experience in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or a related field.
  • Strong experience training ML models in low-data regimes.
  • Experience with active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, uncertainty estimation, or related data-efficient learning methods.
  • Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets.
  • Experience with multimodal learning or methods that combine heterogeneous data sources.
  • Ability to reason about data acquisition strategy, not only model fitting.
  • Strong scientific judgment and ability to connect model behavior to experimental decisions.
  • Practical experience with PyTorch, JAX, scikit-learn, or equivalent ML tools.
  • Ability to collaborate across ML, data, computational science, experimental, and drug discovery teams.

Bonus Points For
  • Drug discovery experience, especially in molecular optimization, screening, or design-make-test-learn workflows.
  • General understanding of pharmacology, biochemistry, or mechanisms of molecular activity.
  • Experience with DEL, high-throughput screening, medicinal chemistry, assay data, simulation-derived features, protein or structure-based features, text or literature features, or scientific images.
  • Experience with closed-loop experimentation, autonomous labs, or agent-driven scientific workflows.
  • Experience with generative molecular design, candidate prioritization, or batch selection workflows.
  • Familiarity with causal inference, optimal experimental design, decision theory, or Bayesian methods.
  • Comfort working with frontier ML techniques where standard out-of-the-box approaches are insufficient.

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$228,000-$358,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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