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Statistical Learning Jobs in Texas (NOW HIRING)

AVP, Machine Learning & Modeling

Irving, TX ยท On-site

$156K - $290K/yr

Model Development and Deployment Oversee the design, development, and validation of statistical ... Foster a culture of innovation, continuous learning, and collaboration while ensuring alignment ...

AVP, Machine Learning & Modeling

Irving, TX ยท On-site

$156K - $290K/yr

Model Development and Deployment Oversee the design, development, and validation of statistical ... Foster a culture of innovation, continuous learning, and collaboration while ensuring alignment ...

$4.5K - $5.8K/wk

Use statistics, machine learning (e.g. deep learning, NLP) to extract patterns from various kinds of datasets through innovative and rigorous research * Implement algorithms in high-quality code

Knowledge of genomic selection, genomic prediction, GWAS, quantitative genetics, and statistical learning for crop improvement. Experience integrating multi-omics datasets (genomics, transcriptomics ...

Statistics Tutor

Lubbock, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Arlington, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

San Marcos, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Fort Worth, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Irving, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Houston, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Bryan, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Statistics Tutor

Carrollton, TX ยท Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Statistics tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

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Showing results 1-20

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.

Junior Machine Learning Engineer

Plano, TX โ€ข On-site

Full-time

Posted 9 days ago


Job description

The Junior ML Engineer will support the Company's data science function by developing, validating, and maintaining machine learning models and the data pipelines behind them. This is a hands-on, applied role: you will work with real data on problems that directly shape the product and the business, and you will be expected to explain what your models do and why they can be trusted.
You will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on.
This role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering.

Key Responsibilities
  • Develop, test, validate, and maintain machine learning models under the guidance of senior team members.
  • Build and maintain data pipelines and analytical datasets on the Company's cloud data platform.
  • Evaluate model performance rigorously and document assumptions, methods, and limitations.
  • Support statistical analysis, forecasting, and experimentation to inform business decisions.
  • Present technical findings clearly to non-technical audiences.
  • Contribute to standards for model documentation, validation, and monitoring.
Qualifications
  • Bachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus.
  • Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code.
  • Hands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar)
  • Strong SQL, including window functions and multi-table joins.
  • Solid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem.
  • Hands-on experience with:ย 
    • Gradient-boosted trees (XGBoost, LightGBM)
    • Logistic regression, support vector machines, k-nearest neighbors
    • Clustering methods (k-means and others)
  • Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection
  • Experience taking a model from development into a scheduled or production environment
  • Docker, CI/CD, and workflow orchestration experience
  • Ability to explain model behavior, including feature importance, calibration, and limitations.
  • Ability to gather and present technical results to a non-technical audience.
  • Proven experience as a machine learning engineer or in a similar role is a plus.
  • Fintech, trading, or financial services background is a plus.