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Statistical Learning Jobs (NOW HIRING)

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 ...

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 ...

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 ...

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 ...

... learning, SPSS Excellent communication and interpersonal skills Qualifications Master's degree/Ph.D. in Statistics, Mathematics or related field with two to five years of experience OR a Bachelor ...

Deep understanding of statistical learning methods * Strong communications and organizational skills * 4+ years of applicable research experience * Bachelor's degree required, Ph.D. desired #LI-SK1 ...

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 ...

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 cities are hiring for Statistical Learning jobs?

Cities with the most Statistical Learning job openings:

What states have the most Statistical Learning jobs?

States with the most job openings for Statistical Learning jobs include:

Infographic showing various Statistical Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Huck Institutes of the Life Sciences NSF NCEMS Postdoctoral Scholar

Penn State University

University Park, PA • On-site

Full-time

Re-posted 6 days ago


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Job description

APPLICATION INSTRUCTIONS:
  • CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.
  • CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
  • If you are NOT a current employee or student, please click "Apply" and complete the application process for external applicants.

Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.
This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.
POSITION SPECIFICS
Postdoctoral Scholar (Machine Learning or Artificial Intelligence in Molecular and Cellular Biology)
The National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) at Penn State University (ncems.psu.edu) seeks an outstanding scientist to fill a Postdoctoral Scholar position in Machine Learning or Artificial Intelligence housed within the Huck Institutes of Life Sciences at Penn State University.
NCEMS is an NSF-funded interdisciplinary research center positioned at the interface of data science, molecular and cellular biology, and quantitative physical sciences. The Center provides leadership in the integration of diverse, publicly available datasets to gain insights into emergent phenomena in molecular and cellular biology.
About the Position
The successful candidate, in collaboration with faculty and research staff at NCEMS, will conduct original research applying machine learning, artificial intelligence, computer science, statistical learning, or related computational approaches to problems in molecular and cellular biology. Research may involve large-scale public datasets, biological data harmonization, representation learning, predictive modeling, foundation models, network analysis, graph-based learning, uncertainty quantification, scientific machine learning, or interpretable AI.
This position is full time, on-site at the Penn State University Park campus. This position does not permit remote work. This is a term appointment funded for one year from the date of hire, with an excellent chance for renewal based upon mutual agreement.
Requirements
A PhD in Computer Science, Data Science or a closely related field is required. The successful candidate will have a strong foundation in machine learning, artificial intelligence, statistical learning, scientific computing, data science, or related computational approaches. Candidates must have experience developing or implementing machine learning methods for large-scale data analysis, predictive modeling, or biological discovery. Expertise in Python is expected. A demonstrated ability to conduct research and produce scholarly outputs, as evidenced by publications in peer-reviewed journals. Familiarity with molecular and cellular biology concepts is also desirable. Candidates must be independent self-starters, have a strong work ethic, strong interpersonal and written communication skills, and the ability to work well in a collaborative team environment.
Application Process
Applications must be submitted electronically in a single PDF and should include:
  • A cover letter
  • Curriculum vitae (CV)

BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
BENEFITS
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being.
For more detailed information, please visit our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university's teaching, research, and service mission.
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