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Data Scientist Machine Learning Jobs in Texas (NOW HIRING)

Judge Group, Inc.

Data Scientist/ ML engineer

Irving, TX · Hybrid

$68 - $73/hr

Data Scientist / Machine Learning Engineer, GenAI We are not accepting C2C or 1099 arrangements. Location: Charlotte, NC or Irving, TX Work Model: Hybrid (3 days onsite per week) Duration: 12-month ...

New

Beth Page tech

Data Scientist

Houston, TX · On-site

Data Science & Machine Learning: * Strong foundation in mathematics, statistics, and machine learning * Experience with exploring and extracting insights from multi-dimensional datasets * Proficiency ...

Braze

Forward-Deployed Data Scientist II

Austin, TX · Hybrid

$98K - $164K/yr

Experience: 3-5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing ...

Select Minds LLC

Data Scientist

Dallas, TX

$65 - $75/hr

Roles & Responsibilities . 6+ years of experience in Machine Learning and Data Science. Strong understanding of Generative AI, Retrieval Augmented Generation, Agentic Workflow, Statistical methods ...

Victory

Data Scientist

Austin, TX

About the Data Scientist position We are looking for a skilled Data Scientist who will help us ... Create predictive models and machine-learning algorithms * Modify and combine different models ...

Victory

Data Scientist

Austin, TX · On-site +1

About the Data Scientist position We are looking for a skilled Data Scientist who will help us ... Create predictive models and machine-learning algorithms * Modify and combine different models ...

Lantern

Data Scientist

Dallas, TX · On-site

As a Data Scientist , you will be responsible for assisting our clients envision, design, develop, and deploy machine learning solutions in Microsoft Azure. As part of a small, dynamic team, you will ...

Apple

Applied Scientist

Austin, TX · On-site

... Machine Learning, or Data Scientist roleFamiliarity with a brand range of quasi-experimental Causal Inference techniques such as diff-in-diff, synthetic control method, panel analysis, regression ...

Mastech Digital

Data Scientist

Irving, TX · On-site

$65 - $70/hr

We are currently seeking a Data Scientist for our client in the Retail domain. We value our ... In this role, you will build and deploy machine learning models, design and analyze A/B tests, and ...

Purple Drive Technologies

Data Scientist

Plano, TX · On-site

Data Scientist Location: San Antonio, TX / Plano, TX (Onsite) Employment Type: Contract Experience ... This role requires strong expertise in Python, R, SQL, statistical modeling, and machine learning ...

RealPage, Inc.

Data Scientist

Richardson, TX · Remote

$116K - $198K/yr

Machine Learning & Data Science * Develop, evaluate, and deploy predictive and generative models for real production use cases * Perform feature engineering and data preparation for modeling ...

RealPage, Inc.

Data Scientist

Richardson, TX · On-site

$116K - $198K/yr

Responsibilities Machine Learning & Data Science * Develop, evaluate, and deploy predictive and generative models for real production use cases * Perform feature engineering and data preparation for ...

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$34.9K

$114.3K

$183.1K

How much do data scientist machine learning jobs pay per year?

As of Jun 10, 2026, the average yearly pay for data scientist machine learning in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

What is a Data Scientist Machine Learning job?

A Data Scientist specializing in Machine Learning (ML) uses statistical methods, algorithms, and computational power to analyze data and create predictive models. They work with large datasets to identify patterns, train machine learning models, and improve decision-making processes. Responsibilities often include data cleaning, feature engineering, model selection, and performance evaluation. They may collaborate with engineers and business teams to deploy models in real-world applications. Strong skills in programming (Python, R), ML frameworks (TensorFlow, Scikit-learn), and data visualization are essential.

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

To excel as a Data Scientist Machine Learning, you need a strong proficiency in statistics, programming (typically Python or R), and a solid understanding of machine learning algorithms, usually backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications in data science or machine learning, is commonly expected. Analytical thinking, problem-solving skills, and effective communication are vital soft skills in this profession. These qualifications combine to drive impactful insights and enable the successful development and deployment of machine learning models in business environments.

Is ML a high paying job?

Data Scientist Machine Learning roles are generally well-paid due to the specialized skills required, such as programming in Python or R and knowledge of algorithms. Salaries vary by experience, location, and industry, but they tend to be higher than average for tech roles, reflecting the demand for expertise in machine learning and data analysis.

Is 40 too late for data science?

Data scientists can enter the field at any age, including 40 or older, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and machine learning tools. Age is less important than demonstrated expertise and the ability to adapt to evolving technologies.

Which 5 jobs will survive AI?

Data Scientist Machine Learning roles are likely to persist as they require advanced analytical skills, domain expertise, and the ability to interpret complex models. Jobs that involve creative thinking, emotional intelligence, and tasks requiring human judgment—such as healthcare professionals, educators, and skilled trades—are also expected to remain resilient despite AI advancements.

What are the typical day-to-day responsibilities of a Data Scientist Machine Learning?

On a typical day, a Data Scientist specializing in Machine Learning might gather and preprocess data, design and implement machine learning models, and evaluate their performance to solve real-world problems. They often collaborate with data engineers, software developers, and business stakeholders to translate business objectives into technical solutions and integrate models into existing systems. Other responsibilities can include visualizing data insights, conducting experiments to tune algorithms, and staying current with new developments in the field. The work is highly collaborative and iterative, requiring clear communication with various teams to ensure project goals are met efficiently.

Do data scientists do machine learning?

Yes, data scientists often use machine learning techniques to analyze data, build predictive models, and extract insights. Proficiency in programming languages like Python or R and understanding of algorithms are essential skills for applying machine learning in their work.
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Data Scientist Machine Learning jobs near you
Sr. Principal Data Scientist / Machine Learning Engineer

Sr. Principal Data Scientist / Machine Learning Engineer

Ascentt

Plano, TX • On-site

Full-time

Posted 3 days ago

Job description

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.
Job Summary
We're looking for an exceptionally skilled and experienced Sr. Principal Data Scientist / Machine Learning Engineer to lead and deliver high-impact AI/ML projects across Automotive domain. The ideal candidate will have a deep understanding of data science and machine learning tools, techniques, and algorithms, coupled with a proven track record of successfully leading projects from conception to deployment. This role demands strong client-facing communication skills and the ability to translate complex technical concepts into tangible business value.
Key Responsibilities
  • Technical Leadership & Strategy:
  • Serve as a primary technical expert and thought leader in Data Science and Machine Learning.
  • Define and drive the technical strategy for AI/ML initiatives, identifying high-value opportunities for optimization, predictive analytics, and process improvement across diverse use cases.
  • Architect and oversee the development of robust, scalable, and production-ready DS/ML models and solutions.
  • Stay at the forefront of the latest advancements in DS/ML, especially those applicable to various industries and large-scale data problems.
  • Project Leadership & Delivery:
  • Lead end-to-end DS/ML projects, including requirements gathering, data exploration, model development, validation, deployment, and monitoring.
  • Define project scope, timelines, and deliverables, ensuring successful execution within budget and schedule constraints.
  • Mentor and guide junior and mid-level data scientists and ML engineers, fostering a culture of technical excellence and continuous learning.
  • Drive MLOps best practices for reliable and efficient model deployment and lifecycle management.
  • Client Management & Communication:
  • Act as a trusted advisor to clients and internal stakeholders, understanding their business challenges and translating them into solvable DS/ML problems.
  • Effectively communicate complex analytical findings, model performance, and business recommendations to both technical and non-technical audiences.
  • Manage client expectations, present progress reports, and ensure stakeholder satisfaction.
  • Facilitate workshops and discovery sessions to identify new opportunities for AI/ML adoption.
  • Use Case Development & Problem Solving:
  • Lead the identification, prioritization, and execution of complex AI/ML use cases that drive significant business impact.
  • Apply deep analytical skills to dissect complex problems, derive actionable insights from data, and design innovative solutions.
  • Develop and implement models for:
  • Predictive Analytics: Forecasting, risk assessment, and anomaly detection.
  • Optimization: Improving efficiency, resource allocation, and decision-making.
  • Pattern Recognition: Identifying trends, segments, and relationships within large datasets.
  • Automation: Leveraging ML for intelligent process automation and enhanced operational efficiency.
  • Tool & Algorithm Proficiency:
  • Demonstrated expertise in a wide range of DS/ML tools and platforms (e.g., Python, R, TensorFlow, PyTorch, scikit-learn, Spark, AWS Sagemaker, Azure ML).
  • Deep understanding and practical application of various machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning, deep learning, time series analysis, NLP, computer vision).
  • Proficiency in data manipulation, SQL, and working with large, complex datasets from various sources.

Qualifications
  • Master's or Ph.D. in Data Science, Machine Learning, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative field.
  • 8+ years of progressive experience in Data Science and Machine Learning roles, with at least 3-5 years in a leadership or principal-level capacity.
  • Demonstrated experience leading multiple end-to-end DS/ML projects successfully from concept to production.
  • Proven track record of managing client interactions, presenting technical solutions, and influencing strategic decisions.
  • Expertise in Python programming (NumPy, Pandas, Scikit-learn, Keras/TensorFlow/PyTorch).
  • Strong understanding of statistical modeling, experimental design, and hypothesis testing.
  • Experience with cloud platforms (AWS, Azure, GCP) and MLOps principles.
  • Excellent communication, interpersonal, and presentation skills.

Preferred Qualifications
  • Experience with real-time data processing and streaming analytics.
  • Knowledge of various industry verticals and their unique data challenges (e.g., finance, healthcare, retail, logistics, manufacturing).
  • Experience with large-scale data architectures (e.g., data lakes, data warehouses, distributed computing).
  • Publications or presentations in relevant fields.

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