Staff ML Data Scientist

As a Staff ML Data Scientist at KoBold Metals, your primary responsibilities will include architecting and implementing foundational data science models for processing vast geospatial data sets. You'll collaborate with geoscientists and engineering teams to improve our machine learning capabilities and enhance data processing pipelines. Additionally, leading a team of field engineers and data scientists, you’ll coordinate our global airborne data collection programs, setting clear goals and technical objectives to guide mineral exploration initiatives.

To qualify for the Staff ML Data Scientist role at KoBold Metals, candidates should have at least 5 years of experience in a related field such as software engineering, data science, or ML engineering. The ideal candidate will possess a strong background in managing technical teams and building production-quality data processing solutions. Proficiency in Python, including experience with array-based packages, and a deep understanding of machine learning concepts are also essential.

KoBold Metals uses advanced machine learning algorithms to process diverse data sources, including hyperspectral and lidar imagery, to inform mineral exploration decisions. By deploying innovative data science models, we can analyze terabytes of data to identify potential mineral resources. Collaboration between our data scientists, geoscientists, and engineers ensures that our machine learning capabilities are effectively integrated into our exploration programs.

The work environment at KoBold Metals is dynamic and collaborative, reflecting the fast-paced nature of an early-stage company. Staff ML Data Scientists are encouraged to take ownership of large projects, interact with cross-functional teams, and engage in continuous learning. Our culture promotes intellectual curiosity, innovation, and teamwork, allowing employees to explore various aspects of mineral exploration and contribute to impactful technology development.

As a Staff ML Data Scientist at KoBold Metals, you will have numerous opportunities for career growth, including the potential to lead more substantial projects and expand your technical expertise in a rapidly evolving field. You will work closely with experienced professionals, gaining insights into mineral exploration and applying cutting-edge technologies. Additionally, as KoBold continues to grow, there will be expanded opportunities for leadership and management roles within the team.

When explaining machine learning to someone non-technical, I would use analogies and simple language. For instance, I might say that machine learning is like teaching a computer to make predictions or decisions based on data, similar to how we learn from experience. I would also emphasize real-world applications, such as how we use data to find new mineral deposits, making the explanation relatable.

One major challenge is the variability and complexity of geological data, which can often be noisy or incomplete. Additionally, integrating multiple data sources, such as hyperspectral and lidar information, while ensuring the accuracy of models can be complex. It requires a deep understanding of both machine learning and geology, as well as strong collaboration with domain experts.

My approach to leading a team is centered on open communication, setting clear expectations, and fostering a collaborative environment. I believe in empowering team members by involving them in decision-making processes and encouraging innovative thinking. Regular check-ins and feedback sessions are essential to ensure that we’re aligned with our goals and can swiftly address any challenges.

I have significant experience utilizing distributed computing resources to handle large datasets effectively, especially using tools like Spark and Dask. I would discuss specific projects where scaling operations improved performance, emphasizing how I optimized data processing workflows to ensure efficiency and result accuracy.

In anomaly detection, I typically employ statistical methods and machine learning techniques such as clustering and isolation forests. I ensure to conduct thorough exploratory data analysis to understand data characteristics before implementing these strategies. Furthermore, I continuously validate models against new data to adapt to potential changes in underlying patterns.

I prioritize tasks by aligning them with company objectives and the strategic goals of our exploration projects. I utilize a combination of impact analysis and team input to ensure that we generate maximum value. Regular team meetings to review progress help us stay on track and recalibrate priorities as needed.

I frequently work with Python and its vast ecosystem, including libraries like NumPy, SciPy, and Scikit-learn for prototyping models. For large-scale applications, I utilize Spark for data processing. I also value visualization tools like Matplotlib and Seaborn to communicate data insights and model results effectively to stakeholders.

In a recent project, I led the development of a multi-modal machine learning pipeline for mineral detection using hyperspectral imaging. By collaborating with geoscientists, we integrated diverse data sets, leading to a successful pilot program that improved our detection rates significantly when compared to traditional methods. The results not only validated our approach but also attracted new strategic partnerships.

I maintain my knowledge in machine learning and data science by regularly attending industry conferences, participating in online courses, and following leading research publications. I also engage in professional networks and communities, allowing me to share insights and learn about innovative techniques from peers.

I am motivated by the profound impact that technology can have on sustainable resource management and energy transitions. Working in mineral exploration allows me to blend my passion for machine learning with a purpose that promotes environmental sustainability and drives positive change in the industry.