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Reliability Data Scientist Jobs (NOW HIRING)

... reliability. Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases.

The Data Scientist partners closely with Operations, Engineering, Capacity Planning, Finance ... High-impact analytical models that improve operational efficiency, reliability, and decision ...

Data Scientist, NA

Denver, CO · On-site

$140 - $150/hr

## Data Scientist, NAApplylocations: Denver, Coloradotime type: Full timeposted on: Posted ... High-impact analytical models that improve operational efficiency, reliability, and decision ...

The Data Scientist partners closely with Operations, Engineering, Capacity Planning, Finance ... High-impact analytical models that improve operational efficiency, reliability, and decision ...

... reliability. Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases.

Required : • Prior experience in equipment reliability, predictive maintenance or physics-based ... Data Science or related field • 2-5 years of experience applying data science modeling or ...

Leidos is seeking a Site Reliability Engineer (SRE) Data Engineer supporting the largest IT service ... science, information systems, software engineering, mathematics, or a related technical discipline ...

Role Name: Data Scientist Location: Charlotte, NC / hybrid Type of hire: Contract Key ... ensure data availability and reliability * Utilize AWS analytics services Athena Redshift ...

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Reliability Data Scientist information

See salary details

$37.5K

$122.7K

$196.5K

How much do reliability data scientist jobs pay per year?

As of Aug 27, 2026, the average yearly pay for reliability data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a reliability data scientist?

A Reliability Data Scientist is a professional who applies data science techniques to assess, predict, and improve the reliability and performance of systems, products, or processes. They analyze large datasets to identify failure patterns, root causes, and opportunities for preventive maintenance. By using statistical models and machine learning, Reliability Data Scientists help organizations reduce downtime, optimize maintenance schedules, and enhance overall operational efficiency.

What are the key skills and qualifications needed to thrive as a reliability data scientist?

To thrive as a Reliability Data Scientist, you need a strong background in statistics, machine learning, data analysis, and reliability engineering, often supported by a degree in engineering, mathematics, or computer science. Familiarity with programming languages like Python or R, statistical analysis tools, and reliability modeling software such as Weibull++ is typically required. Strong problem-solving abilities, attention to detail, and clear communication skills help you translate complex data insights into actionable strategies for cross-functional teams. These skills are vital for accurately predicting system failures, optimizing maintenance, and driving improvements in product reliability and operational efficiency.

How does a reliability data scientist typically collaborate with engineering and operations teams to improve system reliability?

A Reliability Data Scientist works closely with engineering and operations teams by analyzing large volumes of equipment and process data to identify potential failure patterns and root causes. They often participate in cross-functional meetings, share predictive models, and translate complex findings into actionable recommendations for maintenance schedules or design improvements. This collaboration ensures that technical insights are aligned with practical constraints and operational needs, ultimately enhancing system uptime and performance. Effective communication and a strong understanding of both data science and engineering principles are key to success in this collaborative environment.

What is the difference between Reliability Data Scientist vs Data Analyst?

AspectReliability Data ScientistData Analyst
Required CredentialsTypically requires a degree in data science, statistics, or engineering; certifications in reliability or data analysis are a plusUsually holds a degree in statistics, mathematics, or related field; certifications vary
Work EnvironmentWorks in industries like manufacturing, aerospace, or energy, focusing on reliability and predictive modelingWorks across various industries, analyzing data to support business decisions
Employer & Industry UsageUsed by engineering and maintenance teams to improve system reliabilityUsed by marketing, finance, and operations teams for insights and reporting

The Reliability Data Scientist specializes in analyzing data to predict and improve system reliability, often working closely with engineering teams. In contrast, Data Analysts focus on interpreting data to support business decisions across various sectors. While both roles require strong analytical skills, the Reliability Data Scientist emphasizes predictive modeling and reliability metrics.

More about Reliability Data Scientist jobs
Infographic showing various Reliability Data Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Reliability Data Scientist - Turbine Operations

Houston, TX • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 16 days ago


Job description

About Solaris Energy Infrastructure

Solaris Energy Infrastructure, Inc. (NYSE:SEI) provides scalable equipment-based solutions for use in distributed power generation as well as the management of raw materials used in the completion of oil and natural gas wells. Headquartered in Houston, Texas, Solaris serves multiple U.S. end markets, including energy, data centers, and other commercial and industrial sectors.


About the Opportunity

Solaris is seeking a mid-level Data Analyst / Data Scientist to support turbine operations and engineering reliability efforts. This role bridges data engineering, statistical/ML modeling, and turbine domain knowledge to improve fleet reliability, reduce unplanned downtime, and provide operational and financial visibility to engineering and business leadership. The ideal candidate is comfortable moving between building dashboards for business stakeholders and developing predictive models for equipment health.


Essential Functions

Reporting & Business Analytics

  • Build and maintain P&L dashboards tracking turbine fleet financial performance, availability, and cost drivers
  • Translate operational data into clear financial and operational KPIs for engineering and business leadership
  • Support monthly/quarterly reporting cycles with accurate, timely data

Downtime & Reliability Data

  • Collect, clean, and structure turbine downtime data from SCADA, CMMS, OEM reporting, and field logs
  • Classify and root-cause downtime events (mechanical, electrical, control system, weather, grid-related, etc.)
  • Maintain a reliable, queryable historical database of outage and maintenance events across the fleet

Predictive Analytics & AI Tools

  • Develop and deploy predictive models (ML-based and statistical) to forecast turbine downtime and component degradation ahead of failure
  • Build anomaly detection and early-warning tools using sensor/operational data (vibration, temperature, pressure, combustion parameters, etc.)
  • Work with engineering to validate model outputs against physical failure modes and OEM guidance
  • Iterate on models as new failure data becomes available; track model performance over time

Reliability Improvement Support

  • Partner with the Reliability Manager and engineering team to identify trends driving forced outages and derates
  • Support root cause analysis (RCA) efforts with data-driven insights
  • Recommend maintenance interval or strategy adjustments based on data trends (RCM/predictive maintenance support)

Cross-Functional Collaboration

  • Work closely with Operations, Engineering, and Asset Management to ensure data pipelines reflect real-world turbine conditions
  • Present findings to technical and non-technical stakeholders, including leadership


Experience/Education

  • Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent experience)
  • 2–5 years of experience in data analysis, with exposure to industrial/energy/manufacturing operations preferred
  • Proficiency in SQL and Python (pandas, scikit-learn, or similar)
  • Experience building dashboards (Power BI, Tableau, or similar)
  • Strong understanding of statistical analysis and predictive modeling techniques
  • Ability to communicate technical findings to non-technical stakeholders
  • Familiarity with time-series forecasting, anomaly detection, or condition-based monitoring techniques
  • Experience with SCADA/historian data (OSIsoft PI, or similar)
  • Exposure to reliability engineering concepts (MTBF, RCM, FMEA)
  • Experience with cloud data platforms (Azure, AWS) and ML deployment pipelines


Key Skills and Qualifications

  • Exceptional communicator – direct and transparent, skilled problem-solver with proven success in building coalitions and avoiding conflicts
  • Total ownership mentality – proactively identifies and removes obstacles across numerous ongoing tasks
  • Independent thinker – provides original thoughts and constantly asking "how can we do this better"
  • Innovative thinker – willingness to consider novel solutions and ability to adapt to change
  • Desirable teammate – impeccable character, humility, and collaborative
  • Relentless – aspires to contribute and achieve his/her full potential


Our CREATORS Culture

At Solaris, we believe that staying true to our core beliefs improves our decision-making, productivity and is key to our individual and collective achievements. Combining your innovative thinking with our core values that encourage Communication, Recognition, Entrepreneurship, Accountability, Teamwork & Transparency, Ownership, Results and Safety, we become CREATORS.


We value your hard work, integrity, and commitment to the Solaris “First in Service & Innovation” culture through competitive pay and benefits packages and ongoing career development.

  • Competitive compensation packages
  • Medical, Dental & Vision benefits
  • Disability Insurance
  • Company paid Life and AD&D insurance with supplemental offerings
  • Company matching 401(k) retirement plan
  • Paid time off, including 10 paid holidays
  • Career Progression
  • Tuition Reimbursement

This job overview is not all inclusive. In addition, Solaris reserves the right to amend this job overview at any time. Solaris is an Equal Opportunity Employer.