Senior machine learning engineer - energy
About us
We are the independent expert in assurance and risk management. Driven by our purpose, to safeguard life, property, and the environment, we empower our customers and their stakeholders with facts and reliable insights so that critical decisions can be made with confidence.
As a trusted voice for many of the world s most successful organizations, we use our knowledge to advance safety and performance, set industry benchmarks, and inspire and invent solutions to tackle global transformations.
About Energy Systems
We help customers navigate the complex transition to a decarbonized and more sustainable energy future. We do this by assuring that energy systems work safely and effectively, using solutions that are increasingly digital. We also help industries and governments to navigate the many complex, interrelated transitions taking place globally and regionally, in the energy industry.
About the role
Energy Management s Analytics & Data Science team works to accelerate the transition toward a carbon-free future through software and analytics. We are looking for a Senior Machine Learning Engineer to help us accomplish this mission.
Working with the Analytics & Data Science team in DNV Energy Management s Technology group is more than just a job; it s an opportunity to be part of a collaborative community where you can learn, grow, and thrive. Join a dynamic and diverse technology team that values innovation, impact, and sustainability. Help us bridge the gap between advanced analytics systems and user-facing web applications that support demand side management, demand flexibility, and transportation electrification programs!
This role is based at our DNV office in Medford, MA; Chalfont, PA; New York, NY; Rochester, NY; Troy, NY; San Diego, CA; Oakland, CA; Vegas, NV; and Phoenix, AZ offices presenting a dynamic hybrid schedule where employees will typically spend three (3) days per week working from either a DNV office. Further details regarding role-specific requirements will be shared during the interview process. Other DNV offices may also be considered.
What You'll Do:
- As a Senior Machine Learning Engineer, Energy Systems, you will build and deploy scalable machine learning solutions that support utilities, renewable developers, grid operators, and clean energy programs.
- You will partner with data scientists, data engineers, analytics engineers, and software developers from the US and internationally to take models from experimentation to production, ensuring they are performant, reliable, and impactful in the energy domain. This position will coordinate and collaborate across multiple time zones.
- Your work will directly enable data-driven decisions that improve grid reliability, increase energy efficiency, accelerate decarbonization, and support the clean energy transition.
- Applications must clearly demonstrate relevant, hands-on experience in both ML engineering.
- Direct experience working with energy systems, utilities, grid operations, renewable energy, energy efficiency programs, or energy-market data is strongly preferred.
What we offer
- Generous paid time off (vacation, sick days, company holidays, personal days)
- Multiple Medical and Dental benefit plans to choose from, Vision benefits
- Spending accounts FSA, Dependent Care, Commuter Benefits, company-seeded HSA
- Employer-paid, therapist-led, virtual care services through Talkspace
- 401(k) with company match
- Company provided life insurance, short-term, and long-term disability benefits
- Education reimbursement program
- Flexible work schedule with hybrid opportunities
- Charitable Matched Giving and Volunteer Rewards through our Impact Program
- Volunteer time off (VTO) paid by the company
- Career advancement opportunities
**Benefits vary based on position, tenure, location, and employee election**
For California, Washington, New York, Washington, D.C., Illinois, and Maryland: DNV provides a reasonable range of compensation for this role. The actual compensation is influenced by a wide array of factors, including but not limited to skill set, level of experience, and specific location. For the states of California, Washington, New York, Washington, D.C., Illinois, and Maryland only, the starting pay range for this role is $145,000 - $155,000.
DNV is a proud equal opportunity employer committed to building an inclusive and diverse workforce. All employment is decided on the basis of qualifications, merit, or business need, without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. DNV is committed to ensuring equal employment opportunity, including providing reasonable accommodations to individuals with a disability. US applicants with a physical or mental disability who require a reasonable accommodation for any part of the application or hiring process may contact the North America Recruitment Department ([email protected]). Information received relating to accommodation will be addressed confidentially.
For more information :
About you
What is Required
- Bachelor s degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience)
- Minimum 5 years of hands-on experience building and deploying machine learning models in production
Demonstrated Azure cloud experience, including one or more of the following:- Azure Machine Learning
- Azure Databricks or Spark
- Azure DevOps (CI/CD)
- Azure Containers
Strong proficiency in:
- Python (scikit-learn, pandas, numpy, MLflow, etc.)
- PySpark (Databricks)
- ML Ops tooling and model deployment frameworks
- SQL
- Experience working collaboratively with cross-functional engineering and analytics teams
- Ability to manage multiple concurrent projects
- Excellent written and verbal communication skills in English.
- We conduct pre-employment drug and background screening.
What is Preferred
- Experience supporting ML solutions in the energy industry, including utilities, grid operations, renewables, demand-side programs, or energy market
- Experience with time-series models, anomaly detection, clustering, or forecasting
- Understanding of interpretability, fairness, model governance, and responsible AI
Requisition #cmidzmx91000x02l7bjsv8pvq
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