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Associate Machine Learning Jobs in Texas (NOW HIRING)

As a Senior Associate, you will focus on building meaningful client connections and learning how to ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

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As a Senior Associate, you will focus on building meaningful client connections and learning how to ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

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Associate Machine Learning information

See Texas salary details

$22K

$115.8K

$283.9K

How much do associate machine learning jobs pay per year?

As of Jun 8, 2026, the average yearly pay for associate machine learning in Texas is $115,791.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,300.00 and $160,500.00 per year, depending on experience, location, and employer.

What is the difference between Associate Machine Learning vs Data Scientist?

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Engineer, and why are they important?

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by Associate Machine Learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What does an Associate Machine Learning Engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.
What are the most commonly searched types of Machine Learning jobs in Texas? The most popular types of Machine Learning jobs in Texas are:
What are popular job titles related to Associate Machine Learning jobs in Texas? For Associate Machine Learning jobs in Texas, the most frequently searched job titles are:
What cities in Texas are hiring for Associate Machine Learning jobs? Cities in Texas with the most Associate Machine Learning job openings:
Acceleration Center- Agentic AI and Machine Learning Developer- Experienced Associate

Acceleration Center- Agentic AI and Machine Learning Developer- Experienced Associate

Pwc

Dallas, TX • On-site

$61K - $100K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago


PwC rating

8.3

Company rating: 8.3 out of 10

Based on 73 frontline employees who took The Breakroom Quiz

20th of 57 rated business consultants


Job description

Industry/Sector

Not Applicable

Specialism

Risk Architecture

Management Level

Associate

Job Description & Summary

The Opportunity
As a Risk Architecture- Agentic AI and Machine Learning Developer- Experienced Associate, you will play a pivotal role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Risk Consulting practice, you will apply data, algorithms, and software engineering to build and deploy AI and Machine Learning solutions at scale. Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable.
As an Associate, you will focus on learning and contributing to client engagement and projects while developing your skills and knowledge to deliver quality work. You will be exposed to clients to learn how to build meaningful client connections, manage and inspire others, and grow your personal brand by deepening your technical knowledge of firm services and technology resources. You will be expected to anticipate the needs of your teams and clients, embrace ambiguity, ask questions, and use these challenges as opportunities for growth.
In this role, you will take ownership and consistently deliver quality work that drives value for our clients and success as a team. You will build a brand for yourself, opening doors to more opportunities.
Responsibilities
- Designing and implementing AI and machine learning solutions to transform raw data into actionable insights
- Developing and deploying scalable AI models using programming languages such as Python and Java
- Collaborating with team members to integrate AI systems into existing data infrastructure
- Conducting complex data analysis to identify patterns and inform decision-making processes
- Utilizing machine learning libraries like TensorFlow and Scikit-Learn to enhance model performance
- Building and maintaining data pipelines to support AI and machine learning initiatives
- Engaging in data wrangling and data quality assessments to improve data reliability
- Applying natural language processing techniques to develop advanced text analytics solutions
- Participating in client support activities to address AI-related challenges and opportunities
- Continuously learning and adapting to new AI technologies and methodologies to drive innovation
What You Must Have
- At least a Bachelor's degree
What Sets You Apart
- Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics, Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, or Mathematical Statistics, Data Processing/Analytics/Science, Artificial Intelligence and Robotics
- At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials
- Demonstrating proficiency in Python and Java programming languages
- Utilizing machine learning libraries such as TensorFlow and Scikit-Learn
- Engaging in complex data analysis and pattern recognition
- Applying AI implementation skills in client-facing environments
- Developing neural network models for advanced AI solutions

Travel Requirements

Up to 60%

Job Posting End Date

The salary range for this position is: $61,000 - $100,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits-at-a-glanceAs PwC is anequal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law.PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy.Learn more about how we work: https://pwc.to/how-we-workFor only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all.

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