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Temporary Machine Learning Scientist Jobs in Dallas, TX

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st Shift (United ... As one of the first data science hires, you will play a pivotal role in shaping our ML strategy ...

Machine Learning Engineer

Plano, TX · On-site

$120 - $150/hr

Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from ...

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and ... Partners closely with Product, Data Science, Architecture, and Technology teams to deliver ...

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 methods ...

Showing results 41-60

Temporary Machine Learning Scientist information

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

AspectTemporary Machine Learning ScientistData Scientist
CredentialsTypically requires a master's or PhD in computer science, data science, or related fields; experience with machine learning frameworksUsually holds a bachelor's or master's in data science, statistics, or related fields; strong analytical skills
Work EnvironmentProject-based, often contract roles in tech, finance, or healthcare companiesFull-time or contract roles across various industries, focusing on data analysis and insights
Employer UsageHired for specialized machine learning projects, prototypes, or research tasksEngaged in data analysis, reporting, and building predictive models

In summary, a Temporary Machine Learning Scientist focuses on developing and implementing machine learning models on a temporary basis, often requiring advanced credentials and specialized skills. In contrast, a Data Scientist has a broader role in analyzing data and generating insights, with less emphasis solely on machine learning techniques.

What is a temporary machine learning scientist?

Temporary Machine Learning Scientists are professionals hired on a short-term basis to develop, implement, and optimize machine learning models within an organization. They typically work on specific projects or to fill a temporary gap in expertise, often collaborating with data scientists, engineers, and stakeholders. Their responsibilities may include data preprocessing, feature engineering, model selection, and evaluation. These roles are ideal for projects with defined timelines or exploratory research that does not require a permanent hire. Temporary contracts can range from a few months to a year, depending on the project's scope and needs.

What types of projects do temporary machine learning scientists typically work on, and how do they integrate with existing teams?

Temporary Machine Learning Scientists are often brought in to support short-term projects such as data analysis, model prototyping, or improving existing machine learning pipelines. Their work usually involves collaborating closely with data engineers, software developers, and product managers to ensure seamless integration of models into production systems. Since the role is temporary, effective communication and quick adaptation to the team's workflow are crucial. These scientists are expected to rapidly understand the company's data and objectives, deliver actionable insights, and document their work for team continuity after their contract ends.

What are the key skills and qualifications needed to thrive as a temporary machine learning scientist, and why are they important?

To thrive as a Temporary Machine Learning Scientist, you typically need advanced knowledge of machine learning algorithms, data analysis, programming skills (such as Python or R), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and tools for data processing and model deployment is often required, along with experience using cloud platforms such as AWS or Azure. Strong problem-solving abilities, adaptability, and effective communication skills help you quickly integrate into teams and deliver results on short-term projects. These skills ensure you can efficiently contribute to impactful solutions and adapt to rapidly changing project requirements.

What are the most commonly searched types of Machine Learning Scientist jobs in Dallas, TX?

The most popular types of Machine Learning Scientist jobs in Dallas, TX are:

What are popular job titles related to Temporary Machine Learning Scientist jobs in Dallas, TX?

For Temporary Machine Learning Scientist jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Scientist jobs in Dallas, TX look for?

The top searched job categories for Temporary Machine Learning Scientist jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Temporary Machine Learning Scientist jobs?

Cities near Dallas, TX with the most Temporary Machine Learning Scientist job openings:

Infographic showing various Temporary Machine Learning Scientist job openings in Dallas, TX as of August 2026, with employment types broken down into 20% Internship, and 80% Full Time. Highlights an 60% In-person, and 40% Remote job distribution.

Machine Learning Engineer - NJ

Photon

Addison, TX

$54 - $71.50/hr

Full-time

Re-posted 5 days ago


Job description

Summary:

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for customers. You will be responsible for building end-to-end machine learning solutions, implementing models in production, and working with various data frameworks and tools such as Python, Spark, and Databricks.

Key Responsibilities: Analytics Model Development:

  • Analyze use cases and design appropriate analytics models using statistical and machine learning algorithms tailored to specific business requirements.
  • Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights.
  • Apply expertise in data mining and machine learning techniques, including  forecasting, prediction, segmentation, recommendation, and fraud detection.

Data Engineering and Preparation:

  • Extend and augment company data with third-party data to enrich analytics capabilities.
  • Enhance data collection procedures to include necessary information for building analytics systems.
  • Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).

Machine Learning Model Implementation:

  • Implement machine learning models, considering both performance and scalability using tools like PySpark in Databricks.
  • Design and build infrastructure to facilitate large-scale data analytics and experimentation.
  • Work with tools like Jupyter Notebooks for data exploration and model development.

What We're Looking For:

  • Educational Background: Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A PhD is preferred but not necessary.
  • Experience:
    • At least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms such as classification and regression analysis.
    • High-level knowledge of analytics use cases such as language analysis, assortment optimization, promotional planning, dynamic pricing, markdown optimization, labor scheduling, and optimization.
  • Technical Skills:
    • Strong experience with Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
    • Proficiency in using analytics platforms like Databricks for large-scale data processing.
    • At least 4 years of continuous experience with Spark, particularly PySpark implementation.
    • Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.