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Machine Learning Scientist

Role Overview 

Title: Machine Learning Scientist

Hours: Full-Time, Salaried

Location: Salt Lake City, UT, Hybrid

Benefits Eligible: Yes

Manager: Sr Machine Learning Engineer, Rachel Morrison


Mission - Why we need you


Geothermal energy is the most abundant renewable energy source in the world. There is 2,300 times more energy in geothermal heat in the ground than in oil, gas, coal, and methane combined. However, historically it’s been hard to find and expensive to develop. At Zanskar, we’re using better technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid.


The Machine Learning Scientist will play a critical role in accelerating our goals of rapid development of geothermal energy in the US. Although geothermal is abundant, there is extreme geographic variability in how accessible it is at shallow depths that are commercially viable. Finding a resource is a time and labor-intensive process of identifying targets, drilling exploration wells, and collecting other field data. This process requires significant upfront costs with highly uncertain outcomes. The Scientist’s role will be to help Zanskar achieve its mission to augment, automate, and optimize these processes with custom AI tools and algorithms. Successful work will de-risk the process, discover resources that would remain hidden, and drive down development costs.


Outcomes - Problems you’ll solve


The geological factors that make our work difficult and interesting are temperature and depth heterogeneity resulting largely from differences in the underlying source of heat (e.g., magmatism, radioactivity) as well as the processes of heat transfer (e.g., conductive, convective) and where those favorably intersect other commercial factors like access to the grid. In the first six months, you will refine and develop apps & algorithms using various datasets to unlock our ability to predict geothermal potential including:


Model Training and Evaluation: Develop, train, and evaluate models for tasks such as image recognition, object detection, spatial analytics, etc.

Geospatial Data Processing: Work with large-scale geospatial datasets, applying advanced techniques to extract meaningful insights.

Application Development and Deployment: Design and implement web applications to facilitate data ingestion, data exploration and QC, automation tasks, and deployment of machine learning models.

Infrastructure: Leverage our data science infrastructure (cloud compute, Github, Docker, CI/CD pipelines, SQL database, Terraform, etc.)


What we’re looking for


Experienced intersection of ML & Geospatial programming: Minimum 5+ years experience in machine learning and/or data science in a business environment. Master's or Ph.D. in Computer Science, Machine Learning, Geostatistics, Geophysics, or similar preferred. Higher level candidates will be considered for Senior Machine Learning Scientist.


Skills include:

- Proven experience in developing and deploying machine learning models in a business setting, preferably with a focus on deep learning, geospatial applications (e.g., satellite data analysis), and/or computer vision.

- Proven experience developing and deploying dashboards and web applications (e.g., Superset, Panel) in a business setting.

- Strong programming skills in Python and SQL and experience with machine learning libraries/frameworks (e.g., scikit-learn, PyTorch, and/or PyTorchLightning)

- A commitment to coding best practices (version control, peer review, documentation, CI/CD deployment, etc.)

- Familiarity with geospatial data formats, GIS tools, and geospatial libraries (GDAL, GeoPandas, etc.)

- Strong mathematical and statistics background. Bayesian statistics and decision analysis theory are large bonuses.


Strong collaborator: The AI team interacts closely with geoscientists, land leasing, project finance, field technicians, and others to make sure the tools they build create real impact on development decisions. The Scientist must be able to translate business requirements into technical solutions and communicate/collaborate with a variety of stakeholders.


Intrinsically motivated: Zanskar is a team of mission-oriented professionals who live by the value “Blaze a Trail, Leave a Legacy.” We work on problems no one else has solved before, so we need curious self-starters with a strong penchant for solving complex ML problems.



Location and Benefits

- The position is based out of our headquarters in Salt Lake City, Utah, and is a hybrid position


Benefits include:

- Paid holidays

- 15 days PTO + PTO accrual increase based on tenure

- 3 days sick leave

- Medical, dental and vision coverage

- 401k 

- Stock options

- Growth opportunities at a company with a direct impact in displacing carbon emissions


Equal Opportunity Employer 

Zanskar is an equal-opportunity employer and complies with all applicable federal, state, and local fair employment practice laws.

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Average salary estimate

$110000 / YEARLY (est.)
min
max
$90000K
$130000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Scientist, Zanskar

Are you passionate about harnessing the power of machine learning to drive innovation in renewable energy? Join Zanskar as a Machine Learning Scientist in Salt Lake City, Utah, and be a part of a dynamic team on a mission to revolutionize geothermal energy exploration. With geothermal energy being the most abundant renewable resource, Zanskar aims to make it a significant contributor to a carbon-free electrical grid. As a Machine Learning Scientist, you will dive into complex geological data, augmenting and automating the resource discovery process through custom AI tools and algorithms. Your role is pivotal in identifying geothermal potential by developing models for tasks such as image recognition and spatial analytics, all while utilizing large-scale geospatial datasets. You'll engage in application development, creating web applications for data exploration, and collaborating closely with geoscientists and various stakeholders to make impactful decisions in geothermal resource management. With at least 5 years of experience in machine learning, complemented by a strong background in programming and geospatial analysis, you’ll bring valuable insights to our team. At Zanskar, we prioritize collaboration, innovation, and a commitment to coding best practices, so your expertise in Python, SQL, and machine learning libraries is essential. Enjoy benefits including generous PTO, health coverage, and opportunities to grow in a mission-driven company. Blaze a trail and leave a legacy with us as we strive to make the world greener, one geothermal project at a time!

Frequently Asked Questions (FAQs) for Machine Learning Scientist Role at Zanskar
What are the main responsibilities of a Machine Learning Scientist at Zanskar?

As a Machine Learning Scientist at Zanskar, your primary responsibilities include developing, training, and evaluating machine learning models for tasks like image recognition, handling large geospatial datasets, and building web applications for data exploration. You'll also work with the AI team to create solutions that significantly impact geothermal resource discovery and development.

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What qualifications are required for the Machine Learning Scientist position at Zanskar?

To be qualified for the Machine Learning Scientist role at Zanskar, candidates should have a minimum of 5 years of experience in machine learning or data science within a business context. A Master's or Ph.D. in a related field is preferred, along with strong programming skills in Python and SQL, and experience with deep learning and geospatial applications.

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What programming languages should a Machine Learning Scientist at Zanskar be proficient in?

A Machine Learning Scientist at Zanskar should be proficient in Python and SQL, as these are fundamental for developing algorithms and managing data. Familiarity with machine learning libraries like scikit-learn and PyTorch is also essential for success in this role.

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How does a Machine Learning Scientist collaborate with other teams at Zanskar?

Collaboration is key for a Machine Learning Scientist at Zanskar. You will work closely with geoscientists, project finance experts, and field technicians to ensure that the AI solutions you develop align with the company’s goals and have a tangible impact on resource management decisions.

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What makes Zanskar’s Machine Learning Scientist position unique compared to other tech roles?

Zanskar’s Machine Learning Scientist position stands out due to its focus on renewable energy, specifically geothermal resources. This role combines complex machine learning challenges with a meaningful mission to contribute to a sustainable future, offering a unique opportunity to work on innovative projects that have a positive environmental impact.

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Common Interview Questions for Machine Learning Scientist
What experience do you have with machine learning and geospatial data?

Discuss your past projects involving machine learning applications, particularly those related to geospatial data analysis. Highlight specific tools and libraries you've used, such as PyTorch or GeoPandas, and explain how these experiences prepare you for the Machine Learning Scientist position at Zanskar.

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Can you explain your approach to model training and evaluation?

Your response should detail your methodology for training machine learning models, emphasizing the importance of data preprocessing, selection of appropriate algorithms, and techniques you use for evaluation like cross-validation or A/B testing. Make sure to relate your process to real-world applications.

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How do you ensure collaboration with non-technical stakeholders?

Emphasize your communication skills and how you translate technical concepts into layman’s terms. Provide examples of past experiences where you collaborated on projects, ensuring others understood the implications of your work, thus fostering a productive teamwork environment.

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What is your experience with deploying machine learning models?

Outline your exposure to deployment frameworks and tools like Docker, CI/CD pipelines, or cloud services. Discuss how you've successfully transitioned models from development to production while ensuring they maintain robust performance in real-time settings.

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Describe a challenging project you worked on and how you overcame obstacles.

Choose a specific project that presented significant challenges, particularly in machine learning or geospatial contexts. Describe the obstacles faced, your problem-solving strategies, and the results achieved, demonstrating your resilience and adaptability.

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What programming practices do you adhere to in your work?

Discuss your commitment to coding best practices such as version control, documentation, and peer reviews. Explain how adhering to these practices ensures the code quality and facilitates collaboration within a team, particularly in a dynamic environment like Zanskar.

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How would you handle a situation where your model's predictions are significantly off?

Explain your process for troubleshooting model performance issues. Discuss methods such as examining feature importance, refining training datasets, and iterative testing to improve accuracy, emphasizing a systematic approach to problem-solving.

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What role does data quality play in your machine learning projects?

Stress the importance of high-quality, accurate data in training effective machine learning models. Discuss methods for data cleaning, validation, and exploration before training, highlighting how these practices impact overall project success.

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Can you provide an example of how your work has impacted business decisions?

Share a concrete example where your machine learning solutions led to informed business decisions. This could be about resource allocation, project viability, or operational efficiency and illustrate the direct link between your contributions and business outcomes.

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What attracts you to the Machine Learning Scientist position at Zanskar?

Express your passion for renewable energy and your fascination with the potential of geothermal resources. Highlight how Zanskar's mission aligns with your values and professional aspirations, showcasing your enthusiasm for contributing to innovative projects in a meaningful way.

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DATE POSTED
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