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Remote Data Science Jobs in Draper, UT (NOW HIRING)

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Lead Data Scientist

Holladay, UT · On-site +1

The Data Science and Analyticsteam is looking for a Lead Data Scientist. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution;

Inside Real Estate

Senior Data Analyst

Murray, UT · On-site +1

$80K - $101K/yr

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field ... Potential to work in a remote setting; * Exciting/energetic work environment and fun, creative ...

TSMG

AI/ML Data Contributor

Salt Lake City, UT · Remote

Participate in remote assignments or attend on-site sessions when required * Follow project ... Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or ...

TSMG

AI/ML Data Contributor

Salt Lake City, UT · Remote

Participate in remote assignments or attend on-site sessions when required * Follow project ... Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or ...

Harris Company

Facility Data Analyst

Salt Lake City, UT · On-site +1

$56K - $85K/yr

This is a hybrid-remote position that can be based out of the Harris corporate headquarters in St ... What we're looking for in you Bachelor's degree in computer engineering, computer science ...

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Remote Data Science information

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

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

Can I work remotely in data science?

Yes, data science is a field that often offers remote work opportunities. Many companies hire data scientists to work remotely, requiring skills in programming, data analysis, and tools like Python or R. Remote data science roles typically involve collaboration through online platforms and may require strong communication skills.

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

Can a data scientist work fully remote?

Yes, many data scientists work fully remote, especially in companies that prioritize flexible work arrangements. Remote data science roles often require strong communication skills, proficiency with collaboration tools, and the ability to work independently on projects using programming languages like Python or R. However, some positions may require occasional in-person meetings or on-site presence depending on company policies.

Is 40 too late for data science?

Remote data science roles are open to candidates of various ages, and starting a career at 40 is possible with relevant skills in programming, statistics, and machine learning. Many professionals transition into data science later in life by gaining certifications and building portfolios, making age less of a barrier in this field.

What Are the Qualifications to Get a Remote Data Science Job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

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

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

How can I make $100,000 a year working from home?

Remote data scientists can earn $100,000 or more annually by gaining advanced skills in machine learning, programming languages like Python or R, and data visualization tools. Building a strong portfolio, obtaining relevant certifications, and gaining experience in high-demand industries can help achieve this income level while working remotely.
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What are the most commonly searched types of Data Science jobs in Draper, UT? The most popular types of Data Science jobs in Draper, UT are:
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What cities near Draper, UT are hiring for Remote Data Science jobs? Cities near Draper, UT with the most Remote Data Science job openings:
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Infographic showing various Remote Data Science job openings in Draper, UT as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution.
Staff Data Scientist- Pricing Science

Staff Data Scientist- Pricing Science

CSC Generation

Salt Lake City, UT • Remote

Full-time

Re-posted 7 days ago

Job description

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.
 
Reports to: Director of Finance and Business Intelligence
Location: Remote — US or Canada
About the Role
As our Staff Data Scientist, you will design and ship production pricing systems such as demand forecasting, price elasticity modeling, dynamic pricing and the experimentation infrastructure needed to measure whether they actually work.
 
This is a hard, high-stakes problem: your models will directly influence margin and revenue decisions across a portfolio of brands operating at scale. You will own the full arc from framing ambiguous business problems as well-defined ML tasks through to monitoring models that hold up in production.
 
At six months, success looks like at least one pricing model shipped to production with measurable business impact and an experimentation framework in place that your stakeholders trust. If you have spent time building pricing systems from the ground up, not just consuming them, and you care deeply about rigorous causal inference and honest model evaluation, this role was written for you.
What You'll Do
  • Design and build production ML systems for pricing, demand forecasting, and related revenue problems
  • Frame ambiguous business problems as well-defined ML tasks with clear success criteria and measurable outcomes
  • Set the standard for model evaluation, validation, and monitoring — including knowing when CV metrics are misleading and when holdout testing is the only honest answer
  • Build robust predictive models across classification, regression, time series, and causal inference
  • Identify and prevent data leakage, overfitting, and other failure modes before they reach production
  • Design and analyze experiments to measure causal impact of pricing decisions
  • Debug models that fail in production — understand why they fail, not just that they do
  • Translate model limitations, uncertainty, and risk clearly to both technical and non-technical stakeholders
  • Partner with product, engineering, and business teams to ensure ML solutions solve real problems
Required Qualifications
  • 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact.
  • Deep experience in pricing, demand forecasting, or revenue optimization — you have built these models end-to-end, not just consumed them.
  • Expert-level Python and SQL.
  • Deep understanding of ML fundamentals beyond API-level usage, including model evaluation, validation, and failure mode diagnosis.
  • Strong grounding in causal inference and experimental design, including the ability to distinguish correlation from causal result.
  • Ability to work with messy, real-world data and make pragmatic tradeoffs under ambiguity.
  • Familiarity with cloud ML platforms (GCP/Vertex AI or AWS/SageMaker).
  • MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field.
Preferred Qualifications
  • Experience in e-commerce, retail, marketplace, or pricing-intensive industries such as airlines, ride-sharing, or fintech.
Why Join
The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
  • Portfolio-Level Impact: Your models will influence pricing and margin decisions across a $1B+ portfolio of brands — the output of your work is visible at the executive level from day one.
  • AI-First Skill Building: Get hands-on with production ML infrastructure, causal inference at scale, and the Genesis platform — building a modern, applied ML skill set on real retail data problems.
  • Ownership: You will own the full problem from framing through production, with the autonomy to make technical decisions and the stakeholder access to see them through.
  • Competitive Benefits (CAN): Comprehensive benefits including paid time off, RRSP match, group benefits, and employee discounts across portfolio brands.
  • Competitive Benefits (US): Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.
Interview Process
  1. Recruiter Screen: 30-minute call to cover your background, the role, and logistics.
  2. Hiring Manager Interview: Conversation with the Director of Finance and Business Intelligence focused on your pricing science experience, approach to ambiguous ML problems, and how you've driven production impact.
  3. Technical / Case Discussion: Deep dive into a pricing or demand forecasting problem — expect questions on model evaluation, causal inference, and production failure modes. Cross-functional stakeholders may join.
  4. Executive Interview: Final conversation with senior leadership.
  5. Reference Checks: Conducted in parallel with the final stages where possible.
  6. Offer: We move quickly for the right candidate.
For US-based candidates, this posting is intended for candidates that reside in the following states:
AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.
 
For Ontario applicants, please note that this posting is for an existing vacancy.
 
The CSC Generation family of brands provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, provincial, state or local laws. 
 
The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact hrbenefits@cscshared.com.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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About CSC Generation

Sourced by ZipRecruiter

CSC Generation is a multi-brand technology platform based in Merrillville, IN, United States. The organization operates in the retail sector and utilizes technology to save retail companies from going into bankruptcy, while also offering consumers the ability to lease their purchases. Founded by serial entrepreneur, Justin Yoshimura, CSC Generation has leveraged its proprietary technology and customer database to quickly revitalize distressed retail brands. The company's mission revolves around the concepts of reinvention and innovation as it aims to redefine traditional retail and direct-to-consumer models in today's digital age. Notably, the company has, to date, acquired several brands such as DirectBuy, Killion, and most notably, Z Gallerie, growing fast within the e-commerce sector.

Company size

501 - 1,000 Employees

Headquarters location

Merrillville, IN, US

Year founded

2016

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