June 14, 2021

Discovering Data Science Career Paths with TransAlta and AltaML

Discovering Data Science Career Paths

Data Science at TransAlta

Here at TransAlta, we have been conducting data analysis for longer than you might expect; some brilliant engineers, business users and traders (who would never call themselves data scientists but are doing work that would qualify as such) wanted to get more out of their data and naturally started transitioning to more data analysis and data science tools and methods to accomplish the company’s goals.

We have people who would be described as data scientists not just within the Data and Innovation department but also in the Operations Diagnostic Centre and the Trade Floor. There are other teams that are thinking about hiring more data-related roles such as finance and HR.

Leaders might not be hiring for a role specifically called “data scientist.” Applicants should keep an eye out for “analyst” and “automation” roles – these are different but often have a lot of overlapping requirements. The most recent person who would be considered a data scientist was hired for the role of “wind analyst.” We understand how many additional opportunities there are to solve problems using even more advanced data science knowledge and are seeking skilled problem solvers.

So far with the AltaML Applied AI Lab Associates, we have tackled issues such as predicting environmental conditions that might lead to ice formation, detecting anomalous behaviour in our operational equipment to find issues before they become bigger problems, automated cleaning of streaming sensor data to improve the accuracy of our KPIs, and predicting environmental conditions connected to power supply and demand to help sell our power more effectively. All of these are niche, industry-related challenges that require data scientists to be integrated with our subject matter experts within the company – it is difficult to outsource.

Calgary has a lot of engineering talent and engineers often have high data literacy and critical thinking skills which makes them ideal candidates to reskill and side-step to data science.

Applied AI Lab Associate Perspectives 

Data science is one of the fastest growing STEM careers as organizations across sectors build teams to help them derive insights from their data. At AltaML, we see huge potential for applied AI in power generation, and we connected with our Associates to get their perspectives as a result of their Applied AI Lab experience with TransAlta teams. 

They all emphasized the importance, in an applied context, of data scientists working closely with domain experts. Iman Amini, AltaML Associate ML Developer, noted that “domain knowledge is vital in setting reasonable project goals, collecting relevant data, relevant feature selection and model evaluation and deployment. Lack of domain knowledge can lead to having an impossible project or overfitted models due to irrelevant feature incorporation.” 

Jingwen Huang, AltaML Associate ML Developer, identified domain expertise as being particularly important in the problem identification stage as “it helps to identify the right size problem where ML can be applied.” She also noticed that domain expertise helps maintain alignment in language which makes it easier to help drive strategic business decisions. 

David Lin, AltaML Associate ML Developer, emphasized the benefits of domain knowledge for effective communication with clients (e.g. using domain specific terms) and understanding the data which makes exploratory data analysis (EDA) significantly more efficient. “Cleaning, preprocessing, visualizations, tables, all these tasks require some level of understanding of the data,” says David. “In my opinion, EDA is the task most significantly impacted by the knowledge of domain expertise, and EDA encompasses 80% of what we do!” 

Within the team, the Associates have complementary backgrounds, allowing them to work collaboratively with AltaML colleagues and TransAlta teams. Iman has a Masters degree in Electrical and Computer Engineering and his masters thesis was focused on machine learning methods for anomaly detection. Jingwen has a Bachelor of Science degree in Electrical and Electronics Engineering and developed her analytical skills, data science skills, and business acumen from the financial service industry and from online learning platforms. David has a Bachelor of Science in Statistics, and a diploma in data science from Lighthouse Labs. 

Working with TransAlta teams within the Applied AI Lab has been an eye opener for the Associates in a couple of respects. For David, this was his first experience with the “real life problem of large and messy data, though it was a blessing in disguise” as he learned how to deal with “big” data as well as Databricks and Spark. Iman realized that AI & ML methods can be applied almost anywhere. “As long as we can obtain data, we can get insight from this data, put it into a model and use it to benefit the companies,” said Iman. “Therefore, I can see a lot of opportunities in applying data science to the traditional industries.” Jingwen commented on the value of hands-on experience with a real-world use case where “I work collaboratively in a team setting, to build, train and test forecasting models in the Azure cloud environment” and that weekly client presentations have boosted her data storytelling capabilities.

We asked our Associates to consider what advice they would give to those looking for a career in this field. David commented that while “having a strong data science background is definitely essential (python, statistics, EDA experience, modelling etc.), you don't NEED to excel in all these skills to succeed in this field. Learning is probably one of the largest parts about being a data scientist. Learning new tools, platforms, methods and accumulating skills on the job is extremely important.” Iman recommended “start your career by following the application of AI in your field of study. Your domain knowledge can help you see the potential that AI can bring into your field.” Jingwen advised “ if you enjoy solving problems creatively, have a basic understanding in any of mathematics, statistics, analytics, data science, and possess business acumen; a collaborative team player with an eagerness to learn, I think you will do great.”

To explore a career with TransAlta, check out:

About the Applied AI Lab

The Applied AI Lab was created to accelerate applied AI development through a machine learning and data science internship. The AI Lab is a collaborative industry-led initiative, partnering with ATB, Suncor Energy, Spartan Control, and TransAlta. It helps bridge the talent gap between academic and applied skills by providing interns with guidance and access to real business problems, data, and industry partners. Over the 3-year program term approximately 240 individuals will have the opportunity to gain hands-on experience in applying AI.

For more information contact:

Danielle Gifford, Senior Manager of the Applied AI Lab

[email protected]