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Internship Applied Scientist Machine Learning Jobs in Texas

The Senior Machine Learning Scientist develops advanced algorithms and models to extract valuable insights from complex data sets. This role combines deep technical expertise in machine learning with ...

Sr Data Scientist

Fort Worth, TX · On-site

$90 - $100/hr

Data Scientist - Machine Learning & Generative AI Location: Fort Worth, TX 76155 (Hybrid) Durations: 6-12+ Months with possible extension and conversions. Job Overview: We are seeking a Data ...

D. preferred) in Computer Science, Machine Learning, or a closely related field. * Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

New

Machine Learning Engineer

Stafford, TX · On-site

$120 - $180/hr

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

New

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

Showing results 41-60

Internship Applied Scientist Machine Learning information

What types of projects do internship applied scientists in machine learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

What are the key skills and qualifications needed to thrive as an internship applied scientist in machine learning, and why are they important?

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What does an internship applied scientist in machine learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.
What are the most commonly searched types of Applied Scientist Machine Learning jobs in Texas? The most popular types of Applied Scientist Machine Learning jobs in Texas are:
What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Texas? For Internship Applied Scientist Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Internship Applied Scientist Machine Learning jobs in Texas look for? The top searched job categories for Internship Applied Scientist Machine Learning jobs in Texas are:
What cities in Texas are hiring for Internship Applied Scientist Machine Learning jobs? Cities in Texas with the most Internship Applied Scientist Machine Learning job openings:

Senior Machine Learning Scientist

Cotality

Austin, TX • Hybrid

Full-time

Medical, Life, Retirement, PTO

Posted 6 days ago


Job description

At Cotality, we are driven by a single mission-to make the property industry faster, smarter, and more people-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.

Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.

Job Description:

The Senior Machine Learning Scientist develops advanced algorithms and models to extract valuable insights from complex data sets. This role combines deep technical expertise in machine learning with strategic thinking to drive innovation and solve challenging business problems through data-driven approaches.

Job Responsibilities:

  • Model Development: Design, develop, and implement sophisticated machine learning models and algorithms to address complex business challenges.

  • Research Leadership: Lead research initiatives to explore cutting-edge machine learning techniques and methodologies.

  • Data Analysis: Analyze large, diverse data sets to identify patterns, trends, and insights that drive business value.

  • Cross-Functional Collaboration: Partner with cross-functional teams to understand business requirements and translate them into actionable technical solutions.

  • Pipeline Engineering: Develop and optimize data processing pipelines for data preprocessing, feature engineering, model training, and evaluation.

  • Data Integration: Evaluate and integrate new data sources to continuously enhance model performance and business insights.

  • Performance Monitoring: Continuously monitor production model performance and implement technical improvements as needed.

  • Stakeholder Communication: Present complex findings, insights, and recommendations clearly to both technical and non-technical stakeholders.

  • Mentorship: Mentor junior data scientists and machine learning engineers, providing technical guidance and driving knowledge sharing across the team.

  • Documentation: Create and maintain rigorous documentation for models, methodologies, and technical processes.

Job Qualifications:

  • Education: Advanced degree (Master's or PhD) in Computer Science, Statistics, Mathematics, a related quantitative field or equivalent work experience.

  • Experience: 3+ years of relevant industry work experience.

  • Programming & Frameworks: Strong programming skills in Python and extensive experience with machine learning frameworks and libraries (e.g., PyTorch, scikit-learn).

  • Production ML: Demonstrated expertise in developing, deploying, and maintaining machine learning models in production environments.

  • Cloud & Big Data: Proven experience working with big data technologies and major cloud computing platforms (GCP, AWS, Azure).

  • Statistical Proficiency: Strong foundational understanding of statistical analysis, experimental design, and hypothesis testing.

  • Data Storytelling: Exceptional verbal, written, and listening skills with a demonstrated ability to communicate complex data insights clearly to diverse audiences.

  • Navigating Ambiguity: Excellent problem-solving skills with a proven track record of delivering results while working with ambiguous business requirements.

  • Distributed Computing: Hands-on experience with distributed computing frameworks (e.g., Apache Spark, Ray, Dask, or Hadoop) to scale machine learning workloads and process massive datasets.

#LI-Hybrid

Annual Pay Range:

111,900 - 130,000 USD

Application Window:

This opportunity is expected to remain posted through the date identified below, subject to business needs.

Thrive with Cotality

At Cotality, we offer more than just a job, we provide a benefits experience designed to support your whole self. From a flexible working model to competitive time off and standout health coverage with meaningful perks and growth opportunities, our package is built to help you thrive at work and in life.

Highlights, depending on role classification, include:

  • Time off: Generous PTO and 11 paid holidays, plus well-being and volunteer time off.

  • Family Support: Up to 16 weeks of fully paid parental leave and a baby stipend.

  • Health: Multiple medical plan options with mental health and wellness support offerings.

  • Retirement: 401(k) with company match and vesting after one year.

  • Financial Perks: $400 annual well-being stipend and tuition assistance up to $5,250.

  • Extras: Recognition Rewards, Referral bonuses, exclusive discounts and more!

Cotality is an Equal Opportunityemployer committed to attracting and retaining thebest-qualified people available, without regard torace, color, religion, national origin, gender, sexualorientation, gender identity, age, disability or statusas a veteran of the Armed Forces, or any other basisprotected by federal, state or local law. Cotalitymaintains a Drug-Free Workplace.

Cotality is fully committed to a work environment that embraces everyone's uniquecontributions, experiences and values. We offer anempowered work environment that encouragescreativity, initiative and professional growth andprovides a competitive salary and benefits package. We are better together when we support and recognize our differences.

Privacy Policy

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By providing your telephone number, you agree to receive automated (SMS) text messages at that number from Cotality regarding all matters related to your application and, if you are hired, your employment and company business. Message & data rates may apply. You can opt out at any time by responding STOP or UNSUBSCRIBING and will automatically be opted out company-wide.

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