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Statistical Learning Jobs (NOW HIRING)

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Senior Machine Learning Engineer

Arlington, VA · On-site

$155K - $195K/yr

Our projects span multiple different data modalities and incorporate advanced algorithms, deep learning, and statistical techniques to uncover patterns in social media, structured and unstructured ...

Point72

Quantitative Research Intern

Seattle, WA

This is an opportunity for students and researchers of advanced data modeling and statistical learning methods to apply these techniques to market prediction and systematic trading. JOB ...

Astera Institute

$150K - $300K/yr

Think both mathematically and empirically about problems of runtime inference in gradient-trained networks, with an eye to the extensive literature on statistical learning and an open mind to the ...

QuinStreet

Algorithms Engineer

Foster City, CA · On-site

$129K - $149K/yr

Apply statistical and Machine Learning tools to improve our ability to identify, analyze, and interpret trends or anomalies and alerts. Produce well engineered software components to implement ...

Point72

Quantitative Research Intern

New York, NY · On-site

This is an opportunity for students and researchers of advanced data modeling and statistical learning methods to apply these techniques to market prediction and systematic trading. JOB ...

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Statistical Learning information

What are the key skills and qualifications needed to thrive as a statistical learning specialist?

To thrive as a Statistical Learning Specialist, you need a strong background in statistics, probability, and machine learning, typically supported by an advanced degree in statistics, mathematics, computer science, or a related field. Expertise with programming languages such as Python or R, experience with statistical software (e.g., SAS, MATLAB), and familiarity with data analysis libraries are essential. Critical thinking, problem-solving, and effective communication skills help translate complex data insights into actionable business strategies. These competencies are crucial for extracting meaningful patterns from data and driving data-informed decision-making.

How do professionals in statistical learning typically collaborate with data scientists and domain experts on projects?

Professionals in statistical learning often work closely with data scientists and domain experts to ensure that the models they develop are both statistically sound and practically relevant. Collaboration usually involves joint problem definition, sharing data insights, and iterative feedback on model performance. Statistical learning experts contribute their knowledge of algorithms and statistical methods, while data scientists handle data pre-processing and engineering, and domain experts provide context to interpret results. This multidisciplinary teamwork helps ensure that solutions are robust and actionable for stakeholders.

What is the difference between Statistical Learning vs Data Analyst?

AspectStatistical LearningData Analyst
Required CredentialsDegree in Statistics, Data Science, or related fieldsDegree in Statistics, Data Science, Business, or related fields
Work EnvironmentResearch, academia, tech companies, data science teamsBusiness, marketing, finance, healthcare organizations
Employer & Industry UsageTech firms, research institutions, startupsCorporations, consulting firms, government agencies
Common Search & ComparisonStatistical Learning vs Data Analyst

Statistical Learning focuses on developing models and algorithms to understand data patterns, often requiring advanced statistical and programming skills. Data Analysts interpret data to generate reports and insights, typically emphasizing data visualization and business understanding. While both roles analyze data, Statistical Learning is more research-oriented and technical, whereas Data Analysts focus on practical data interpretation for decision-making.

What will I become if I study statistical learning?

Studying statistical learning can lead to roles such as data scientist, data analyst, machine learning engineer, or statistician. These positions involve analyzing data, building predictive models, and applying statistical methods using tools like R or Python in various industries.
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Statistical Learning jobs near you
Infographic showing various Statistical Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution.

Research Scientist, Machine Learning (PhD)

New York, NY • On-site

Synaptrix Labs
See more jobs at Synaptrix Labsexternal

Full-time

Medical, PTO

Re-posted 5 days ago

Job description

About Synaptrix:
Synaptrix is building non-invasive brain-computer interfaces by treating neural decoding as a fundamental machine learning problem.
The brain produces extraordinarily high-dimensional, noisy, non-stationary signals generated by an underlying dynamical system that we can only partially observe. We are developing new models, datasets, and hardware to learn these dynamics and translate them into real-time control of computers, communication systems, mobility devices, and eventually a much broader class of machines.
We are looking for exceptional researchers across machine learning, artificial intelligence, applied mathematics, physics, dynamical systems, computational neuroscience, and related fields.
Prior experience in neuroscience or brain-computer interfaces is not required. We care much more about exceptional research ability, mathematical depth, and the ability to develop new approaches to difficult modeling problems.
What You'll Work On
  • Develop new machine learning methods for modeling high-dimensional neural and behavioral data, spanning representation learning, generative modeling, sequence modeling, latent-variable models, and learned dynamical systems.
  • Learn latent structure and dynamics from noisy, non-stationary, partially observed time-series data.
  • Develop approaches to neural decoding that generalize across people, sessions, tasks, and recording conditions.
  • Explore problems at the intersection of deep learning, dynamical systems, system identification, control, information theory, optimization, and statistical learning.
  • Investigate self-supervised and unsupervised learning methods that can take advantage of large quantities of neural data without requiring dense behavioral labels.
  • Design rigorous experiments to understand model scaling, generalization, representation quality, and the limits of non-invasive neural decoding.
  • Build simulations and generative models for studying neural signals and testing hypotheses about learned representations and decoding algorithms.
  • Work closely with researchers collecting large-scale neural datasets and engineers building the sensing hardware that generates them.
  • Translate promising research into real-time systems controlling computers, communication interfaces, wheelchairs, prosthetics, and other machines.
  • Build rigorous, reproducible implementations of research ideas and scale successful approaches to large datasets and compute.
  • Contribute original research that advances both Synaptrix's systems and the broader scientific understanding of neural decoding.
Minimum Qualifications
  • PhD or equivalent demonstrated research ability in machine learning, computer science, applied mathematics, physics, statistics, computational neuroscience, electrical engineering, or a related technical field.
  • Evidence of exceptional ability to conduct original research.
  • Strong mathematical foundations in areas such as linear algebra, probability, optimization, statistics, information theory, or dynamical systems.
  • Strong programming ability and experience implementing and evaluating machine learning models in PyTorch, JAX, or equivalent frameworks.
  • Experience working with high-dimensional, sequential, scientific, sensory, or otherwise complex datasets.
  • Ability to take an ambiguous research problem from first principles through formulation, experimentation, analysis, and implementation.
  • Ability to operate independently, question existing assumptions, and pursue technically ambitious ideas.
Particularly Interesting Backgrounds
You may be an especially strong fit if your work has involved one or more of:
  • Representation learning and self-supervised learning
  • Foundation models
  • Generative modeling
  • Time-series or sequence modeling
  • Latent-variable and state-space models
  • Dynamical systems and system identification
  • Scientific machine learning
  • Inverse problems
  • Reinforcement learning and optimal control
  • Information theory
  • Statistical physics
  • Computational neuroscience
  • Neural signal processing
  • Multimodal learning
  • Large-scale distributed model training

None of these backgrounds is individually required. We are interested in exceptional researchers with unusual technical depth, including people whose previous work has had nothing to do with neuroscience.
Research Culture
We are a small research-driven team working on problems where there is no established playbook. We value first-principles thinking, mathematical and experimental rigor, intellectual honesty, speed, and researchers who are willing to question assumptions about what should be possible with non-invasive neural signals.
We care more about important results than credentials, titles, or adherence to a particular modeling paradigm.
Our goal is to make non-invasive brain-computer interfaces capable enough to restore communication and mobility to people with severe disabilities, and ultimately to create a general interface between the human brain and machines.
What We Offer
  • Competitive salary and meaningful & generous equity ownership
  • Comprehensive health benefits
  • Paid holidays and unlimited PTO
  • Work on ambitious, high-impact problems alongside exceptional researchers and engineers across multiple disciplines
  • High ownership and rapid career growth for team members who deliver outsized impact

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