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Data Scientist Jobs in Reno, NV (NOW HIRING)

Jobtailor

Staff Data Scientist

Carson City, NV · On-site

$180 - $260/hr

Mentor data scientists by improving problem framing, modeling judgment, validation rigor, code quality, and ability to operate independently in ambiguous domains. Requirements * Master's or Ph.D. in ...

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Data Scientist information

See Reno, NV salary details

$37.4K

$122.4K

$195.9K

How much do data scientist jobs pay per year?

As of Aug 24, 2026, the average yearly pay for data scientist in Reno, NV is $122,379.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,200.00 and $135,600.00 per year, depending on experience, location, and employer.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks, data visualization tools, and big data platforms like TensorFlow, Tableau, and Hadoop, as well as certifications in data science, are highly valued. Excellent problem-solving skills, curiosity, and the ability to communicate complex findings clearly set outstanding data scientists apart. These skills and qualities are crucial for extracting actionable insights from data, driving business decisions, and collaborating effectively with stakeholders.

What do data scientists do?

Data scientists collect, confirm, and interpret data to determine useful information for their employer. They help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. The information data scientists get from the records they gather helps businesses make major decisions in critical areas, such as product development, sales and marketing techniques, and client retention. Data scientists are highly educated; the majority of them have at least a master's degrees, and many have doctorates. Data scientists are valuable members of organizations in many different industries, including pharmaceuticals, manufacturing, and banking.

What are some typical projects data scientists work on, and how do they collaborate with other teams?

Data Scientists often work on projects such as building predictive models, analyzing large datasets to uncover trends, and developing data-driven solutions to business problems. They regularly collaborate with cross-functional teams, including software engineers, data engineers, and business analysts, to ensure that their insights are actionable and aligned with business goals. Effective communication and teamwork are essential, as Data Scientists frequently need to present complex findings to non-technical stakeholders and incorporate feedback from various departments.

What is the difference between Data Scientist vs Data Analyst?

AspectData Scientist
Required CredentialsDegree in Computer Science, Statistics, or related field; often requires advanced degrees
Work EnvironmentResearch and development, predictive modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcare, consulting firms
Common Search & ComparisonOften compared due to overlapping skills in data analysis and modeling

Data Scientists focus on building predictive models, advanced analytics, and machine learning, often requiring higher-level technical skills and education. Data Analysts primarily interpret existing data, generate reports, and support decision-making with descriptive analytics. While both roles analyze data, Data Scientists handle complex modeling and predictive tasks, whereas Data Analysts focus on data interpretation and reporting.

Is a data scientist job still in demand?

Yes, data scientist roles remain in high demand across various industries due to the increasing reliance on data-driven decision making. Skills in machine learning, statistical analysis, and programming languages like Python or R are highly valued, and employment opportunities continue to grow as organizations seek to leverage big data for competitive advantage.

What does a data scientist do exactly?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use statistical techniques, programming languages like Python or R, and tools such as SQL and machine learning algorithms to interpret data and solve complex problems.
More about Data Scientist jobs

What are the most commonly searched types of Data Scientist jobs in Reno, NV?

The most popular types of Data Scientist jobs in Reno, NV are:

What are popular job titles related to Data Scientist jobs in Reno, NV?

For Data Scientist jobs in Reno, NV, the most frequently searched job titles are:

What job categories do people searching Data Scientist jobs in Reno, NV look for?

The top searched job categories for Data Scientist jobs in Reno, NV are:

What cities near Reno, NV are hiring for Data Scientist jobs?

Cities near Reno, NV with the most Data Scientist job openings:

Data Scientist jobs near you
Infographic showing various Data Scientist job openings in Reno, NV as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $122,379 per year, or $58.8 per hour.

Staff Data Scientist

Jobtailor

Carson City, NV • On-site

$180 - $260/hr

Other

Posted 8 days ago

Job description

  • Lead high-impact machine learning and feature-development initiatives across device, network, browser, mobile, session, and behavioral intelligence.
  • Own ambiguous fraud and identity risk problems where data quality, label reliability, adversarial behavior, customer impact, and product tradeoffs must be evaluated together.
  • Develop production risk signals and models that balance fraud detection, false-positive risk, coverage, latency, explainability, robustness, and operational maintainability.
  • Build and guide scalable feature-engineering approaches for high-cardinality, sparse, noisy, and platform-dependent telemetry.
  • Investigate complex signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, device fragmentation, and over-linkage risk.
  • Define evaluation methods for Digital Intelligence signals, including holdout design, leakage checks, drift monitoring, adversarial robustness, customer impact analysis, and long-term signal stability.
  • Influence telemetry collection, data contracts, feature logging, model monitoring, and production readiness in partnership with engineering, product, risk, and platform teams.
  • Translate open-ended product, customer, and fraud-risk questions into clear data science approaches, measurable hypotheses, and production-ready signal roadmaps.
  • Raise team standards for feature quality, model validation, explainability, documentation, and risk-signal governance.
  • Mentor data scientists by improving problem framing, modeling judgment, validation rigor, code quality, and ability to operate independently in ambiguous domains.
Requirements
  • Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field.
  • 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.
  • Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems.
  • Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
  • Expert-level SQL skills and extensive experience working with large-scale, complex, noisy datasets.
  • Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools.
  • Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation.
  • Demonstrated ability to work with imperfect labels, delayed outcomes, telemetry artifacts, instrumentation gaps, and changing fraud patterns.
  • Strong judgment across data quality, modeling approach, feature design, explainability, operational complexity, and business impact.
  • Excellent communication skills, including the ability to explain complex data science decisions and risk tradeoffs to technical and non-technical audiences.
Benefits
  • Opportunity to influence telemetry, product direction, and data science standards while mentoring others.
  • Meaningful ownership over ambiguous, high-impact technical problems, from signal strategy and evaluation design to production rollout and long-term signal quality.
Core Competencies

Candidates should emphasize their expertise in machine learning model development, fraud detection, and data science methodologies. Highlighting experience in mentoring teams, managing complex data challenges, and collaborating across engineering and product teams will be crucial.

Highest-signal resume keywords
  • Machine Learning Model Development
  • Fraud Detection
  • Data Science Methodologies
  • Mentoring Data Scientists
  • Collaboration Across Teams
ATS Optimization Keywords Hard Skills
  • Machine Learning
  • Statistical Modeling
  • SQL
  • Python
  • Feature Engineering
Soft Skills
  • Communication Skills
  • Judgment
  • Problem Framing
  • Mentoring
  • Collaboration
Certifications & Qualifications
  • Master’s Degree
  • Ph.D.
Industry Keywords
  • Fraud Detection
  • Identity Verification
  • Cybersecurity
  • Risk Modeling
  • Anomaly Detection
Tools & Technologies
  • Spark
  • PySpark
  • Data Processing Frameworks
  • Telemetry Systems
  • Model Monitoring Tools
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