DATA SCIENTIST: CAREER JOURNEY
After joining the Alithya family in March 2014, Issam Hammad began working on software qualification projects as a member of the engineering team, familiarizing himself with the various nuclear systems and standards used in the firm’s practice. In February 2015, he joined Alithya’s (SWI) team at Ontario Power Generation (OPG) to work on software verification for the Darlington Refurbishment Project.
It was during this time at OPG that the self-learner immersed himself in acquiring knowledge and honing his skills in the field of machine learning, an exercise which earned him a leadership role in advancing Alithya’s first AI engineering project, and one of the first AI projects for the nuclear industry in Canada. In conjunction with Sarah Hall, Ryan Simpson, and Hippolyte, Issam expanded Alithya’s AI expertise into new territory by developing a proof of concept (PoC) to automate the detection of fuel channel defects in nuclear reactors, a task previously conducted manually by multiple highly skilled analysts. In addition to providing potentially significant cost savings in time and human resources, the solution paves the way for future nuclear and AI engineering projects for Alithya, as well as the possibility of expanding it to other industries, including oil & gas.
In January 2018, Issam returned to Alithya’s offices to work on multiple engineering projects incorporating elements of machine learning, big data, predictive analytics, web development, and database development. That same year, he embarked on a journey to further his knowledge and formalize his skills by pursuing a PhD in Hardware AI. In May 2019, his initiatives were rewarded with a promotion to the position of Senior Consultant/Senior Data Scientist at Alithya. And with rapid expansion in the fields of the Internet of things (IoT) and Industrial IoT (IIoT) ahead, he sees a bright future for AI and machine learning and hopes that his work will lead to large engineering projects for Alithya, involving multiple data scientists and engineers.
“My advice to other self-starters is to leverage online machine learning platforms, such as Coursera or Udacity,” he suggests. “However, above all, there is no replacement for work experience and the exposure to different machine learning problems that it offers.”