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Temporary Data Scientist Machine Learning Jobs in Texas

Judge Group, Inc.

Data Scientist/ ML engineer

Irving, TX · On-site

$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 ...

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 ...

Select Minds LLC

Data Scientist

Dallas, TX · On-site

$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 ...

Hp

AI Data Scientist - Enterprise AI

Spring, TX

$130K - $205K/yr

This role sits at the intersection of applied research, machine learning engineering, data science, and business transformation. The ideal candidate combines strong technical expertise in Large ...

Hp

AI Data Scientist - Enterprise AI

Austin, TX

$130K - $205K/yr

This role sits at the intersection of applied research, machine learning engineering, data science, and business transformation. The ideal candidate combines strong technical expertise in Large ...

Victory

Data Scientist

Austin, TX · On-site

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

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 ...

Compunnel

Senior Data Scientist GenAI / RAG

Houston, TX · On-site

Job Summary We are seeking a Senior Data Scientist with a strong background in traditional Machine Learning, Deep Learning, and hands-on experience building enterprise Generative AI solutions. This ...

Apple

Applied Scientist

Austin, TX · On-site

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

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All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Texas

Temporary Data Scientist Machine Learning information

What is the difference between Temporary Data Scientist Machine Learning vs Temporary Data Analyst?

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

What does a Temporary Data Scientist specializing in Machine Learning do?

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

What are the key skills and qualifications needed to thrive as a Temporary Data Scientist Machine Learning, and why are they important?

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary Data Scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.
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Temporary 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

Re-posted 12 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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