ZipRecruiter

Log In

2

Entry Level Data Scientist Machine Learning Jobs in Houston, TX

Westlake

Data Scientist I - Digital

Houston, TX · On-site

Machine Learning and Model Development * With proper oversight, participate in the design and ... Ability to conduct research into data science operational issues, standards, and products as ...

WorkNovas LLC

Senior Data Scientist

Houston, TX · On-site

This requires that you have next to your knowledge of machine learning and/or statistics a good grasp of software development. Next to the data science capabilities and experiences you should be able ...

Innotech

Data Scientist

Houston, TX

$12K - $78K/yr

... and machine-learning engineers Required skills and qualifications * Seven or more years of ... data science * Proficiency with data mining, mathematics, and statistical analysis * Advanced ...

Texas Children's Hospital

Data Scientist, Assoc

Houston, TX · On-site

$56K - $56K/yr

Join a team that's using data, AI, and advanced analytics to shape the future of healthcare. As an Associate Data Scientist, you'll leverage analytics, machine learning, and AI to solve complex ...

Amazon

Data Scientist II - AMZ27028.1

Houston, TX · On-site

$136K - $184K/yr

Position: Data Scientist II Location: Houston, TX Multiple Positions Available: Design and ... machine learning algorithms, genetic algorithms, and neural networks. Validate models against ...

next page

Showing results 1-20

All JobsData Scientist Machine Learning JobsEntry Level Data Scientist Machine Learning JobsEntry Level Data Scientist Machine Learning Jobs in Houston, TX

Entry Level Data Scientist Machine Learning information

See Houston, TX salary details

$35.8K

$117.2K

$187.7K

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

As of Jul 19, 2026, the average yearly pay for entry level data scientist machine learning in Houston, TX is $117,212.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,100.00 and $129,900.00 per year, depending on experience, location, and employer.

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

To thrive as an Entry Level Data Scientist in Machine Learning, you need a solid background in statistics, programming (Python or R), and foundational machine learning concepts, typically supported by a relevant degree in computer science, data science, or a related field. Familiarity with tools and libraries such as scikit-learn, TensorFlow, Pandas, and SQL, as well as experience with data visualization platforms, is highly valuable. Strong problem-solving skills, attention to detail, and the ability to communicate technical findings clearly set candidates apart. These skills are essential for effectively analyzing data, building predictive models, and translating complex results into actionable business insights.

What are entry level data scientist machine learning jobs?

Entry level data scientist machine learning jobs are positions for individuals who are new to the field of data science and machine learning. These roles typically focus on working with data, building and testing machine learning models, and supporting more experienced data scientists. Entry level professionals may clean and analyze data, implement basic algorithms, and help interpret results to inform business decisions. These jobs often require proficiency in programming languages like Python or R, foundational knowledge of statistics, and some experience with machine learning libraries.

What are some common challenges faced by entry-level data scientists working with machine learning models?

Entry-level data scientists often encounter challenges such as cleaning and preparing messy or incomplete datasets, selecting appropriate algorithms for specific problems, and tuning model parameters to achieve optimal performance. In addition, they may need to clearly communicate technical findings to non-technical stakeholders and collaborate closely with team members from engineering, product, and business departments. Gaining experience in version control, reproducibility, and model deployment are also important steps in mastering the end-to-end machine learning workflow.
What are the most commonly searched types of Data Scientist Machine Learning jobs in Houston, TX? The most popular types of Data Scientist Machine Learning jobs in Houston, TX are:
What are popular job titles related to Entry Level Data Scientist Machine Learning jobs in Houston, TX? For Entry Level Data Scientist Machine Learning jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Entry Level Data Scientist Machine Learning jobs in Houston, TX look for? The top searched job categories for Entry Level Data Scientist Machine Learning jobs in Houston, TX are:
What cities near Houston, TX are hiring for Entry Level Data Scientist Machine Learning jobs? Cities near Houston, TX with the most Entry Level Data Scientist Machine Learning job openings:
Entry Level Data Scientist Machine Learning jobs near you
Data Scientist - Remote

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Posted 16 days ago

Job description

NAVA Software solutions is looking for a Data Scientist
Details:
Data Scientist
Location: Houston TX - Remote is ok
Duration: 12 months
Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology
A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:
  • Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
  • Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
  • Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
  • Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
  • Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
  • Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
  • Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
  • Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.
NAVA Software Solutions logo

About NAVA Software Solutions

Sourced by ZipRecruiter

NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

Social media

Facebook

View All NAVA Software Solutions Jobs

Apply