Wind Technologist Subject Matter Expert

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What We Do:

Uptake harnesses the power of underutilized data to empower businesses to make informed decisions. We partner with industry leaders to build predictive analytics software that grows smarter in one industry because of what we learn in another. The result is a powerful platform that identifies problems before they happen, ultimately saving money, time and lives.

Uptake Energy envisions a global energy system where everyone can access dynamic, resilient, and sustainable power. We’re building on Uptake’s platform and talent to solve the challenges of today’s energy marketplace and enabling the transition to the power system of tomorrow.

 What You’ll Do:

The Wind Technologist Subject Matter Expert will leverage his or her engineering expertise of electrical and mechanical wind energy systems to collaborate with our team of data scientists in the training of predictive and diagnostic machine-learning models, corrective and preventive maintenance auto-suggestion algorithms, and to otherwise incorporate subject matter expertise into the Uptake platform. The Wind Technologist will also help to foster relationships with wind industry vendor-partners and support account management in ensuring the successful deployment of software that improves the safety, reliability and profitability of our customers’ power plants.

  • Support the product and data science team in designing advanced software features for diagnostics and optimization of wind turbine systems.
  • Identify industry cutting-edge practices and drive engineering efforts to enhance product and establish Uptake as a thought leader in the sector.
  • Leverage relationships with national labs, universities and industry professional societies, such as IEEE, IEC, CIGRE, AWEA to advance research agenda and maintain technological competitiveness.
  • Attends and actively participates in industry related conferences and committees and build relationships and support business development team efforts.
  • Assist in preparation of white papers for external marketing and internal training of Uptake product and data science teams in principles of wind turbine optimization.

 Qualifications:

  • Master or PhD degree in electrical engineering or mechanical engineering.
  • 5 or more years of private sector wind industry experience.
  • Experience working in a national lab or original equipment manufacturer in a technical capacity is a plus
  • Deep subject matter expertise on either wind resource assessment (aerodynamics, aero elasticity, mesoscale dynamics, CFD analysis) or wind turbine systems including control systems, dynamic mechanical and/or hydraulic systems, generators, gearboxes, blades, main bearing, yaw and pitch systems.
  • Able to analyze vibration data, oil sample reports, and inspections to assess the risk of major component failures, make equipment recommendations and forecast failure rates.
  • Able to quantitatively analyze data derived from SCADA and different software platforms, present results visually and qualitatively, draw conclusions, and make recommendations for action.
  • Experience within industrial application of model predictive control, system identification and/or machine learning is a plus.
  • Experience conducting root cause analyses for mechanical, electrical and structural component failures.
  • Experience leading Big Data projects and familiarity with related technologies is a plus.
  • Strong technical capabilities and a creative, curious, self-driven work ethic.
  • A collaborative mindset, and a record of building successful client relationships and internal relationships with colleagues.
  • Superior written and verbal communication skills—especially a strong ability to translate between technical and business stakeholders.
  • An entrepreneurial spirit, sense of humor, and a passion to help create the energy system of the future.
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Location

We are located in River North just right off the Chicago Brown Line stop. We also provide you with a free shuttle service to/from Ogilvie and Union.

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