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Statistical Learning Jobs (NOW HIRING)

The successful candidate will have a strong foundation in machine learning, artificial intelligence, statistical learning, scientific computing, data science, or related computational approaches.

Senior Machine Learning Engineer

$107K - $146K/yr

ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) * Hands-on experience with ...

The Machine Learning Department has been at the forefront of research in such areas as deep learning, statistical learning, and machine reasoning for almost two decades. The research in our ...

Senior Machine Learning Engineer

$107K - $146K/yr

ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) * Hands-on experience with ...

Machine Learning Researcher

New York, NY · On-site

$154K - $213K/yr

Broad experience across multiple fields is a plus Development experience in Python or R (C, C++, Java, etc is a plus) Deep understanding of statistical learning methods Strong communications and ...

Write statistical programs for use in creating analysis datasets, tables, listings, and figures ... Ongoing training and continuous learning opportunities throughout your career * An environment that ...

Showing results 41-60

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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What other helpful pages are available for Statistical Learning?

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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, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution.

Assistant Professor (12MO), Biostatistics

Brooklyn, NY • On-site

SUNY Downstate Health Sciences University
Health Care and Social Assistance • 1 - 5K employees

$105K - $120K/yr

Full-time

Re-posted 20 hours ago


Job description


The Department of Epidemiology & Biostatistics in the School of Public Health at SUNY Downstate Health Sciences University is seeking a full-time Assistant Professor (12MO). The successful candidate will: Teach biostatistics courses to masters and doctoral students.
Mentor Master of Public Health and doctoral students.
Provide biostatistics support to faculty in the School of Public Health and across Downstate Health Sciences University.
Engage in a focused area of research.
Qualifications
Required Qualifications
  • Terminal degree, PhD, MD, ScD.
  • Experience in teaching Data Science courses, program development, and developing and managing biostatistical support at the institutional level.
  • Experience with data display; spatial analysis and modeling; statistical methods for hierarchical data; longitudinal repeated-measures data; complex survey data; mixed-methods data; quasi-experimental research designs; causal inference methods such as propensity score matching methods, and structural equation models; non-parametric and semi parametric methods; statistical learning methods for prediction and classification; and/or big data analytic expertise in-omics data, imaging data, bioinformatics, and electronic health records data.
  • Expertise in machine learning and Al approaches to public health research is also desirable.
  • Successful attainment of extramural funding and demonstrated research expertise, as evidenced by a substantial publication record in high-quality journals.
  • Or, a satisfactory equivalent combination of experience, education and training to the above.