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Flexible Data Science Jobs in Utah (NOW HIRING)

This person will primarily work as part of the larger Data Science team interacting Product ... We also offer flexible time off for our exempt team members + 13 paid holidays * Paid parental ...

This person will primarily work as part of the larger Data Science team interacting Product ... We also offer flexible time off for our exempt team members + 13 paid holidays * Paid parental ...

This person will primarily work as part of the larger Data Science team interacting Product ... We also offer flexible time off for our exempt team members + 13 paid holidays * Paid parental ...

Whether your background is in data science, astrophysics, economics, biostatistics, operations ... Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects ...

Bachelors degree in Computer Science, Engineering, Statistics, Data Science or related field plus ... Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts * Paid Training, Paid ...

Bachelors degree in Computer Science, Engineering, Statistics, Data Science or related field plus ... Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts * Paid Training, Paid ...

Bachelors degree in Computer Science, Engineering, Statistics, Data Science or related field plus ... Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts * Paid Training, Paid ...

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Flexible Data Science information

What are the key skills and qualifications needed to thrive as a Flexible Data Scientist, and why are they important?

To thrive as a Flexible Data Scientist, you need a strong background in statistics, programming (Python or R), and data analysis, often supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), data visualization tools (such as Tableau or Power BI), and experience with cloud platforms are widely required. Adaptability, problem-solving, and effective communication are crucial soft skills for translating complex data insights into actionable business strategies. These competencies ensure you can handle varied data challenges, deliver valuable insights, and collaborate effectively across dynamic project environments.

How does a flexible data science role typically structure collaboration and workload across multiple projects?

In a flexible data science role, professionals often work on several projects simultaneously, collaborating with cross-functional teams such as engineering, product management, and business stakeholders. This structure requires strong communication skills and the ability to prioritize tasks effectively. The work environment is usually dynamic, with shifting priorities and the need to adapt to different project requirements. Flexibility in both working hours and methodologies enables data scientists to manage deadlines while delivering actionable insights for diverse business needs.

What is flexible data science?

Flexible data science refers to the practice of applying data science methods, tools, and techniques in a way that adapts to different industries, project requirements, and work environments. Professionals in flexible data science may work remotely, part-time, on contract, or across multiple domains, using skills in statistics, programming, and data analysis to solve a variety of problems. This approach allows both employers and data scientists to adjust workflows and deliverables to fit changing business needs and schedules.
What are the most commonly searched types of Data Science jobs in Utah? The most popular types of Data Science jobs in Utah are:
What cities in Utah are hiring for Flexible Data Science jobs? Cities in Utah with the most Flexible Data Science job openings:
Data Science Consultant

Data Science Consultant

DataAnnotation

Salt Lake City, UT • On-site, Remote

$40/hr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr