So weve been sitting here brainstorming some sort of catchy intro for about 20 minutes now, and everything we come up with is either too corporate, or too lame to really come across as anything catchy. So this is what you get, not a super corporate, not super lame intro, which is essentially us saying "Hey, we might not be very good at writing catchy introductions, but were good at being honest and authentic! Come join us on our development journey.

CRWN.ai is a machine learning company that uses sensors to detect, localize, and categorize electrical discharge in real time, without an operator in the field. Thats the how. The why? Identifying the changes in frequency in electrical discharge allows us to give insight into areas of wildfire risk, power outages, and exceptional electrical discharge on powerlines. If this gets walked backwards you start to see how we can look at CRWN as an application to clean energy, through providing real time data to identify areas of inefficiencies in the power grid (and obviously reducing the risk of wildfires is pretty clean and cool too).

Alright, so where do you fit in?

CRWN is currently testing live on transmission lines. We are looking at our roadmap and know that the data is the most important thing to Utilities. You will be responsible for the following:
Visionary Data Strategy: Develop and execute a data strategy that aligns with our business goals. Establish the vision, tools, and processes necessary for data collection, processing, and analysis.

Strong Collaboration Skillset: We dont expect you to know everything. We do expect you to be able to collaborate with industry experts, firmware engineers and hardware engineers to deliver strong product market fit.

Field Data Collection and Integration: Identify and implement data collection methods and sources to gather high-quality data. Ensure the integration of various data streams.
Experimental Design: This will require expensive high voltage lab work in a professional test facility. Understanding experimental design with an emphasis on planning will be just as important as iterating quickly if things are not working.
Data Analysis: Analyze data to identify patterns, trends, and opportunities. Translate data into clear and actionable recommendations for Utilities
And most importantly:

The willingness to dive in and just "figure it out.

Key Responsibilities:

• Data Strategy Development: Define and execute data collection, processing, and analysis strategies tailored to support machine learning and DSP objectives.
• Data Collection & Integration: Identify and implement effective data collection methods, ensuring the integration of diverse data streams for comprehensive analysis. We do this at a high voltage lab that requires in person work in Surrey BC as well as field data collection. The rest is up to you! (An estimate of 70% remote and 30% in person for data collection over the next year).
• Machine Learning & DSP Innovation: Develop, train, and optimize machine learning models and DSP algorithms to extract actionable insights and drive predictions from raw data.
• Real-World Testing & Deployment: Engage directly in fieldwork to collect data, test models, and deploy solutions in dynamic, real-world environments, ensuring product relevance and robustness.
• Pattern Analysis & Insights: Analyze data to uncover patterns, trends, and opportunities, translating findings into actionable recommendations, particularly for utilities and related industries.
• Cross-Functional Collaboration: Work closely with the Head of Product and Tech Lead to align data strategies with business goals and user needs.
• Agile Team Leadership: Participate in daily standups, sprint planning, retrospectives, and reviews while contributing to a cohesive, high-performing data science team.
• Team Development: Assist in hiring, mentoring, and conducting performance reviews for machine learning engineers and data scientists. Foster a collaborative and growth-oriented team culture.

Required Qualifications:

• Proven expertise in digital signal processing techniques, including data filtering, feature extraction, and spectral analysis.
• Strong proficiency in programming languages such as Python or MATLAB with experience in ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Experience designing and deploying machine learning models for real-world applications.
• Demonstrated ability to collect, analyze, and interpret data from diverse sources.
• Familiarity with agile methodologies, including Scrum, and experience leading or participating in sprints.
• Excellent communication and leadership skills, with the ability to mentor and guide junior team members.
• A passion for fieldwork and a deep understanding of how real-world challenges shape product-market fit.

Preferred Qualifications:

• Experience integrating DSP/ML systems with hardware and edge computing platforms.
• Familiarity with deploying ML systems in production environments and optimizing them for scale.

What We Offer:

Besides the Tech we have some pretty sweet perks:

• Flexible working environment: Monday - Thursday 9:30am-1:30pm are hard hours. We use these for predictable collaboration time, but we trust you to get your work done!
• We have an office in Kelowna, B.C. where everyone pops in and out as needed. Some people are there 4 days a week, some are only there once a week (or not at all!). Over the next year we are looking at approximately 3 months of work (total over the year) located in Surrey for data collection, so in person will be required for this.
• 5 weeks paid vacation + Mandatory Christmas shut down
• Healthy benefits and stock options plans!

How to stand out:

Dont worry about a super formal cover letter. Answer one (or all!) of these 3 questions in your cover letter:
1. Tell us about a time when you encountered and overcame a situation of this cant be done
2. Describe a predictive problem for which you have successfully applied advanced DSP techniques and the insight you used to choose your methodology.
3. Demonstrate your commitment to continual learning by discussing the most recent data-science related skill youve developed.