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Remote Optum Data Science Jobs in Texas (NOW HIRING)

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

RealPage, Inc.

Data Scientist

Richardson, TX · Remote

$116K - $198K/yr

Machine Learning & Data Science * Develop, evaluate, and deploy predictive and generative models ... Experience building AI solutions that support customer facing products #LI-REMOTE #LI-JL1 Physical ...

RealPage, Inc.

Data Scientist

Richardson, TX · Remote

$116K - $198K/yr

Responsibilities Machine Learning & Data Science * Develop, evaluate, and deploy predictive and ... Experience building AI solutions that support customer facing products #LI-REMOTE #LI-JL1 Physical ...

Zeta Global

Senior Manager, Data Science & Analytics ZX Data Science & Analytics | US, Remote Zeta Global is looking for a seasoned, external client-engaging Senior Manager, Data Science & Analytics (individual ...

HiddenLayer

Data Scientist

Austin, TX · Remote

Data Scientist Remote [within the US] ABOUT THE ROLE: We're looking for a Data Scientist to join our Data Sciences and ML Engineering team. You'll be building, shipping, and improving the models and ...

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Remote Optum Data Science information

What is the difference between Remote Optum Data Science vs Remote Optum Data Analyst?

AspectRemote Optum Data ScienceRemote Optum Data Analyst
Required CredentialsTypically requires a master's or PhD in Data Science, Statistics, or related fieldsBachelor's degree in Data Analysis, Statistics, or related fields often sufficient
Work EnvironmentFocus on advanced modeling, machine learning, and predictive analyticsData cleaning, reporting, and basic analysis tasks
Employer & Industry UsageUsed in healthcare analytics, predictive modeling, and research within OptumUsed for reporting, data management, and supporting decision-making processes

Remote Optum Data Science roles involve developing complex models and algorithms, requiring advanced degrees and technical expertise. In contrast, Remote Optum Data Analyst positions focus on data reporting and basic analysis, often requiring a bachelor's degree. Both roles support healthcare analytics but differ in scope and technical depth.

What are the most commonly searched types of Optum Data Science jobs in Texas? The most popular types of Optum Data Science jobs in Texas are:
What are popular job titles related to Remote Optum Data Science jobs in Texas? For Remote Optum Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Remote Optum Data Science jobs in Texas look for? The top searched job categories for Remote Optum Data Science jobs in Texas are:
What cities in Texas are hiring for Remote Optum Data Science jobs? Cities in Texas with the most Remote Optum Data Science job openings:
Remote Optum Data Science jobs near you
Data Scientist - Remote

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Posted 9 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.
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About NAVA Software Solutions

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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

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