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U of T’s Data Sciences Institute helps place the data scientists it’s producing

Participants are seeking employment as data and reporting analysts, data coordinators and technicians.

The Data Sciences Institute (DSI) is a tri-campus, multidisciplinary hub for data science at the University of Toronto (datasciences.utoronto.ca). It facilitates research connections, fosters innovation and enhances teaching and learning in data sciences, including emerging data-driven disciplines. The DSI, with the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada, also offers an intensive, 16-week certificate in data science or machine learning software for people with a university or college degree who have three years or more of work experience. They’re learning programming skills in languages such as Python and SQL. The participants are seeking employment in sectors like healthcare as data analysts, reporting analysts, data coordinators and data technicians.

In this article, we interview leaders at two Canadian companies who are hiring certificate-holders from the Data Sciences Institute. They discuss why they’re working with the DSI, and how hiring Data Science Institute participants will benefit their organizations.

 

*article reprint from original in Canada Health Technology 

Photos: Canada Health Technology

 

Javier Diaz, PhD, is head of data science at Phenomic AI Inc., a rapidly growing start-up biotech company that’s devising solutions for combatting cancer. In particular, it aims to raise the survival rate for patients with the hardest to treat solid tumours. Phenomic AI is doing this by identifying new targets in tumours for drugs. The work involves AI and machine learning. The company recently partnered with global pharmaceutical giant Boehringer Ingelheim in a business deal that’s potentially worth more than $500 million to Phenomic AI, which is based in Toronto and Boston.

Canadian Healthcare Technology: What appealed to you about hiring people who have completed the DSI’s certificate?

Javier: What I like about the Data Sciences Institute is that they don’t only train students in terms of technical aspects like programming and machine learning, but also they look for so-called soft skills, and they try to improve that in the students. I also like that the students have backgrounds in different areas, including healthcare. These are the ones we are interested in, as we’re collecting data about cancer. These persons know about cancer, know the cancer terms that biologists and clinicians use, which is not often trivial. Candidates from other places might have more experience with software engineering or machine learning, but they might not be aware of the terminology that is used by biologists or cancer biologists. So, they had the technical skills and the business knowledge, which was great. It saves a lot of time from our side in terms of onboarding them into the team.

Canadian Healthcare Technology: How many people have you hired from the DSI?

Javier: We have one person working already with us from the Institute and one more starting next week.

Canadian Healthcare Technology: Was the DSI sensitive to your needs? Did they filter the candidates they sent your way?

Javier: Yes, I spoke to them about our needs and then they sent me about four or five resumes of candidates that they thought would be relevant. We put out a job description that was shared with the students. The candidates they sent me were outstanding in terms of the business knowledge that they are bringing to the team. They know about cancer biology. They even know about the particular type of technology that we’re using, which is called single cell RNA sequencing. So, I think what made their candidates different from others was the biology knowledge that they have. And then they also know how to program, which is great. They really met our needs. They have the two aspects that we were looking for, the technical and the domain knowledge.

Canadian Healthcare Technology: What kind of work will your new hires from the DSI be doing?

Javier: They are going to help us keep up with onboarding new data from public repositories. So, we have a database with about 150 studies collected. We built some computational tools that will help us to streamline things so that we can onboard more data in a faster way. These two new team members will continue on that, making use of our tools and developing new tools to make this process even faster and more streamlined.

Canadian Healthcare Technology: So, they’re going to continue to develop the database?

Javier: Exactly. They’re going to curate the database – get more data and standardize it. We will identify data that we might be missing for some cancer types and some particular treatments, because we want to keep making our database more inclusive of all cancer types and bigger, and they will help us with that.

Canadian Healthcare Technology: As well as the domain knowledge, do the DSI hires have the technical skills?

Javier: They do know how to program, which is very important. The Data Sciences Institute spend most of the time teaching them how to use Python, and that’s exactly what we need. If you are going to pick only one language, speak Python. Because it’s more standard in the industry, regardless if they stay in the health sciences or they go to banks or finances or other industries. Python is the gold standard in industry.

Sepehr Sisakht is CEO of Shyftbase Inc., a Toronto-based company that produces software for supply-chain management. The five-year-old company has grown quickly by applying new technologies like machine learning (ML) and other forms of artificial intelligence to improve product deliveries and returns in a variety of industries. It is targeting healthcare, which Mr. Sisakht sees as needing modernization in the area of supply chain management. At the time of this interview, the company was about to hire a Data Sciences Institute participant.

Canadian Healthcare Technology: Have you hired a DSI participant?

Sepehr: We are interviewing three candidates. There were quite a few who were interested, but we have one position available. We haven’t finalized it yet, but we will decide on the candidate this week. All three are very, very good.

Canadian Healthcare Technology: What appeals to you about the skills of the participants from the DSI?

Sepehr: Well, they are being educated in data science, but they also have an education in other areas. If you are aspiring to be a really successful data scientist, I think previous experience is very important because you bring all those perspectives and you are able to look at problems from different angles. And with the technical knowledge that they’ve acquired through this program, I think those candidates can definitely excel, compared to a lot of other conventional computer science programs.

Canadian Healthcare Technology: What abilities do you need in a new hire, and do you see these skills in the DSI candidates?

Sepehr: I love to see hints of problem-solving skills and abilities. You want your data scientists to be able to take initiatives and look at problems from different angles. If they’re going to join your team, you want them to add value by looking at the data and being able to solve problems. Technical skills are great, but I think everybody can learn more on the job. On the other hand, not everybody has the drive in them to look at different angles for certain problems. The people from DSI have these problem-solving abilities.

Canadian Healthcare Technology: Do you think it’s productive to hire a person from DSI, who has
completed a short but intensive course in data science, rather than a person who may have done several years in a university-based computer science program?

Sepehr: In the long term, it’s the personality and the drive that you get from the person, rather than technical skill. It might be counterintuitive to a lot of hiring managers, because there’s a lot of focus on the resumes. But it’s somewhat ridiculous in terms of what is expected of candidates these days. Even when hiring for a junior role, everybody’s expecting five years of experience, which is unrealistic.

We’d rather hire someone who can think outside the box. We like to get a sense of their problem-solving skills. At the end of the day, that gives us a good indication of their skill set. And of course, we also look at their personality and whether they’ll fit in our team or not.

Canadian Healthcare Technology: Do you have confidence in the training DSI participants receive?

Sepehr: For sure, I mean U of T is obviously a very credible university and a credible source of talent. I came in contact with the DSI a while ago and learned about their programs. I myself used to do data science mentorship a few years ago, and I worked with aspiring data scientists. Once I learned about this program at the University of Toronto, I reached out and had a conversation. I wanted to work with them, as we had available positions. We wanted to part of their program and draw on their people.

Policy Lab: Experts Highlight Critical Skill Gaps and Innovations in Data Science for Public Health 

In collaboration with the Dalla Lana School of Public Health, the Data Sciences Institute’s (DSI) Policy Lab recently hosted an enlightening panel discussion at Research Day 2024, entitled Translating Data for Decision Making.  

With a mandate to build capacity and demand across the public sector for data-science insights, through collaborations with ministries, agencies and other policy-oriented groups, the Policy Lab seeks to promote discussion on data science issues and the skillsets needed to address solutions.

“Identifying the key skills and qualities required for successful deployment in real-world settings is an ongoing and fluid process,” said Laura Rosella, Policy Lab co-lead (Professor, Dalla Lana School of Public Health and Department of Laboratory Medicine & Pathobiology, Temerty Faculty of Medicine, University of Toronto). 

“As data science tools continue to evolve rapidly, organizations within the modern health industry are increasingly looking for professionals with wide range of skillsets and qualities to take on important challenges they face daily in a responsible way.”

“At Policy Lab, we’re able to create a unique forum for organizational leaders to both identify and discuss areas that need improvement and as well engage in early stage problem-solving on shared issues.”

Panelists at the Translating Data for Decision Making represented a broad range of sectors including government, hospitals and research institutes, including: Michael Hillmer (Assistant Deputy Minister, Digital Analytics and Strategy Division, Ministry of Health and Ministry of Long-Term Care, Ontario Public Service and Associate Professor, Institute of Health Policy, Management, and Evaluation, University of Toronto); Lillian Sung (Canada Research Chair in Pediatric Oncology Supportive Care, Division of Haematology/Oncology, Chief Clinical Data Scientist, The Hospital for Sick Children); Amol Verma (Clinician-Scientist, St. Michael’s Hospital, and Assistant Professor, Temerty Professor of AI Research and Education in Medicine, University of Toronto), and Linbo Wang (Associate Professor, Department of Computer and Mathematical Sciences, University of Toronto Scarborough).  

Panelists discussed the complexities surrounding a number of core issues, including how to best set up a data sciences entity within a health organization and what innovative tools are needed. 

“It’s very hard for a data science team to drive the culture on its own,” said Hillmer. “One of the biggest challenges in the policy world is that issues are rarely one to one. There is almost nothing that answers a question with certainty, so humility is an important trait to have.” 

“One big revolution I would love to see in the data science world is more simulation tools,” he added. “We need to see reactions in different ways. Some tools that use game theory to drive policy development are very interesting and I think the next big development.” 

The ability to understand both broad and specific data insights and solutions within the health sector – essentially an ability to communicate and translate complex data findings – was also identified by panelists as an area of concern. 

“Data serves as a starting point for these conversations [in health],” said Verma. “They don’t tell us how to use the data or what to do but they are a valuable beginning point.” 

“The ways that we use data science, ensuring data quality and presenting it in a simple way that is easy to understand – all of that work is really at the core of the data science field and really why we need the next generation of data sciences to help us in healthcare.

As panelists honed in on key skills and qualities required for successful data science professionals, a number of looming challenges in the modern health organizations were also outlined.  

“One thing is that data literacy [in hospitals] needs to be strengthened,” explained Sung. 

“You cannot advocate for change and make things better unless you have a good understanding of your own data availability and data science capacity.”  

“For instance, there is not a great sense of what data is missing. The data we don’t have is infinite. One example is the lack of systematic data on patient reported outcomes.” 

Click here to learn more about Data Sciences Institute’s Policy Lab initiative. 

Data Sciences Institute’s Research Day Spotlights Evolution and Innovation in Data Science Domains

By: Cormac Rea

Photos: Harry Choi Photography

The data science community gathered for the Data Sciences Institute’s hugely popular Data Science Research Day on October 1 — a celebration of the fusion of data, innovation, and collaboration — for a program packed with engaging lightning talks, poster sessions, discussions, networking activities and interactive panels.  

Providing a platform for the DSI community to showcase their work and cultivate connections with collaborators from academia, industry, and government, the day began with a captivating keynote address delivered by Dr. Stefaan Verhulst on the topic of Navigating the Emergent Data Winter: Reimagining Data Access and Stewardship in the AI Era. 

Verhulst outlined a set of complex challenges for policy creators, decision-makers, and consumers in an increasingly “datafied” world, speaking to broad issues such as data asymmetries, systematic, sustainable, and responsible data collaborations, and what steps can be taken to prevent a future “data winter.”

“We have to reimagine the whole question and decision life-cycle [in data science],” said Verhulst. “What are the best questions to be asking? And we will also need to build the human infrastructure with a new generation of data stewards.”  

“In fact, we need a new paradigm of data stewardship,” concluded Verhulst. “So that we can move from data to decision intelligence. We often gain insight, but then nobody uses it. Can we perhaps advance the process with decision accelerator labs?” 

The Research Day itinerary also featured a series of enlightening lightning talks under the theme, Research Software for Impact. Each speaker presented on their research, how the software support their research, the tool itself, and finally the outcome, impact or primary use.  

The panel included presenters: Jo Bovy (Professor and Canada Research Chair in Galactic Astrophysics, Department of Astronomy & Astrophysics, University of Toronto); Gregory Schwartz (Scientist, Princess Margaret Cancer Centre, University Health Network; Professor of Medical Biophysics, University of Toronto; Canada Research Chair in Bioinformatics and Computational Biology), and Areti Angeliki Veroniki (Scientist, Knowledge Translation Program, St. Michael’s Hospital, Unity Health Toronto, Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto). 

This lightning talk showcased several exciting research projects supported through the DSI Research Software Development Support award. Topics explored included open-source galaxy classification and segmentation apps, an interactive “tree” of single-cell relationships, and a software tool to help expediate health records between health clinicians.  

“It is fascinating to learn  the various ways the DSI Research Software support program is being used to translate and share real “research software for impact,” said Lisa Strug, Director, Data Sciences Institute.    

During the networking lunch, the DSI community also had the opportunity to mingle, engage with, and explore the excellent work of DSI students and trainees who presented their research via posters.  

The posters covered a wide array of projects, showcasing the diversity of research within the DSI community. Among the poster presenters was DSI Graduate Doctoral Fellow, Madeline Bonsma-Fisher, who shared her work entitled Exploring the geographical equity-efficiency trade-off in cycling infrastructure planning. 

“It has been awesome to see all the different work that the DSI supports, the variety of fields and disciplines,” said Bonsma-Fisher. “You really get different ideas here than you would if you were just in your own research silo.”

“I have a background in a very different field, in biophysics, and I work on transportation, so I understand there is a different value in bringing all of these fields together today. I love that I get to see at a high level what people are working on in different areas.”

Another set of intriguing lightning talks opened the afternoon program, with the topic discussion Research to Impact featuring: Sara Allin (Associate Professor, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto); Robert Batey (Professor, Department of Chemistry, University of Toronto); Brice Batomen Kuimi (Assistant Professor, Epidemiology, Dalla Lana School of Public Health), and Bree McEwan (Associate Professor, Institute for Communication, Culture, and Information Technology, University of Toronto Mississauga). 

All of the speakers were supported by DSI funding and the researchers spoke to a variety of projects and how the DSI funding was leveraged to garner external funding and impact public policy. Research projects included: Canadian newcomers and immigrants with diabetes encountering difficulty accessing supportive info and affordable medicine, a DSI seeded project to develop new molecules and a Pan-Canadian Chemistry library, and another DSI funded project that leverages image recognition algorithms to develop a comprehensive database detailing installation of traffic calming measures across major Canadian cities. 

“I work in data science and I came to this event because I was interested in the different approaches people take to tackle similar problems,” said Olexiy Pukhov, a student studying pharmacy and data science at University of Toronto’s Leslie Dan Faculty of Pharmacy.  

“I’ve been exposed to new ideas that I can utilize in my own research to further improve the outcomes that I obtain. There were some very interesting ideas and new methods today that I hadn’t thought about before. Research Day was very useful to me! I learned a lot about innovative ideas and it has armed me with new tools in my toolbox to solve data science problems.”

Research Day concluded with a compelling panel discussion on Translating Data for Decision-Making that aligns with the DSI’s Policy Lab initiative.  

Panelists from a broad range of sectors including government, hospitals and research institutes, including: Michael Hillmer (Assistant Deputy Minister, Digital Analytics and Strategy Division, Ministry of Health and Ministry of Long-Term Care, Ontario Public Service); Lillian Sung (Canada Research Chair in Pediatric Oncology Supportive Care, Division of Haematology/Oncology, Chief Clinical Data Scientist, The Hospital for Sick Children); Amol Verma (Clinician-Scientist, St. Michael’s Hospital, and Assistant Professor, Temerty Professor of AI Research and Education in Medicine, University of Toronto), and Linbo Wang (Associate Professor, Department of Computer and Mathematical Sciences, University of Toronto Scarborough). 

Panelists discussed a variety of perspectives and solutions to big questions, such as: How can one best set up a data sciences entity within a health organization? How do the healthcare sector decision makers get influenced by the data that is available? 

The Data Sciences Institute extends heartfelt thanks to all of its funding partners for their support in making Research Day possible. The inspirational day was a testament to DSI’s mandate to help shape the evolution of the data science field and the University of Toronto’s leadership role in it, bringing data science to new domains, new industries, not-for-profits and to government.

Data Sciences Institute Forges Employment Pathways with Industry Collaborations and Upskilling Certificates

By: Cormac Rea

The Data Sciences Institute (DSI) continues to strengthen its position as a hub for data science training and employment, as professionals from its Data Science and Machine Learning Software Foundations certificates continue to secure jobs and promotions.

At a recent Talent Blitz event, DSI Certificate participants connected with industry representatives seeking candidates who have completed the certificates. Companies that have already hired from prior cohorts were enthusiastic about the skills taught through the certificates and brought job postings specifically for the Data Science Certificate participants.

“Hiring from the Data Science and Machine Learning certificates has been a great resource for our team,” said Javier Diaz-Mejia, Head of Data Science at Phenomic AI.

“The participants brought not only a strong foundation in technical skills but also knowledge of the specialized vocabulary in the field, which has helped us to speed things up. The entire process was seamless, making it easy to find the right fit for our needs. We look forward to continuing this partnership!”

Toronto was recently ranked as the No. 4 tech labour market in North America by CBRE, which found that the region created more jobs than graduates in the past year, signalling data science and AI skills will remain in high demand.

During the DSI Talent Blitz, over 75 participants discussed opportunities with companies from various sectors, underscoring the importance of data science skills across industries. Representatives from ADASTRA, RBC, WSIB, AI TechLife, Aidols Group, Competitive Scale, Sanofi Digital and Shyftbase were among those scouting new talent. These companies engaged with DSI participants who have completed intensive DSI certificates in data science and machine learning software foundations.

With the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada, these 16-week part-time certificates are designed for professionals with three or more years of experience. This initiative aims to address the growing need for data analytics and machine learning skills across industries, providing a pathway to career advancement.

“My sincere gratitude for the excellent training provided through the Data Science Certificate program,” said Data Science Certificate recipient, Krystle Lin.  “The knowledge and skills I gained have been instrumental in helping me secure a position as a Junior Program Analyst at the Ministry of the Environment, Conservation, and Parks (MECP).”

The high demand for DSI upskilling programs continues to grow, with over 350 participants registered since last fall from over 1,000 applicants.   

“There is such a need in the community for this work,” said Lisa Strug, Academic Director of the DSI and Professor in the Departments of Statistical Sciences and Computer Science and the Division of Biostatistics, during her opening remarks at Talent Blitz. She highlighted that the certificates are developed in collaboration with industry partners to ensure that the skills taught address real-world challenges.

“We had the opportunity to connect with some really talented candidates through Talent Blitz and we would like to further nurture this relationship in the future,” added Shivangi Pathak, Talent Acquisition Lead, ADASTRA.

As a result of the DSI certificates’ comprehensive job readiness support and strong employer connections, 37 per cent of participants have secured new employment, received promotions, or transitioned into new roles within six months of completing a certificate

“[Talent Blitz] was a great opportunity to connect face to face with candidates and understand their level of experience and excitement about open roles,” said Hafiz Kanji, Managing Partner, Competitive Scale.

In addition to technical training, DSI’s programs offer job readiness workshops and one-on-one career support to help participants develop critical skills such as resume writing, interview preparation, and networking.

“The job readiness support was incredibly helpful,” said Lin. “The guidance I received on polishing my resume and improving my interview skills made a significant difference in my job search, allowing me to present myself confidently to employers.” 

As DSI continues to expand its industry partnerships, the number of job postings and project-based opportunities for participants is expected to increase. The Institute routinely shares numerous postings with its participants, ensuring that participants not only gain the skills needed to succeed but also have access to aligned employment opportunities.

“To achieve our goal, we need to continue facilitating meaningful engagements between organizations seeking talent with the skilled professionals we are training through the certificates,” said Strug.

As the network of industry partners grows, and the commitment to upskilling professionals deepens, the Data Sciences Institute is solidifying its role as a leading incubator for talent in the data science field. By offering targeted training, job readiness support, and key networking opportunities, DSI continues to help participants transition into high-demand roles.

Join our next Talent Blitz on November 22 to meet accomplished professionals with data science and machine learning skills to grow your team. Please contact dsi.partnerships@utoronto.ca.  

Breaking New Ground: Data Sciences Institute and Ernst & Young Collaborate on Data-Driven Consulting Project with Mitacs Support

Photo (L-R): SUDS scholars Tejas Balaji, Minh Dang, Farah Mikati; Sumaiya Hossain, DSI Partnership & Business Development Officer

A team of Summer Undergraduate Data Science (SUDS) scholars recently embarked on a transformative journey in their careers, utilizing a forward-thinking initiative between University of Toronto’s Data Sciences Institute (DSI) and Ernst & Young (EY) with funding support from Mitacs.

The data-driven EY Project, Leveraging Data Science to Build Consulting Specific Solution Offering, brought together three SUDS scholars to work on the project at EY during the summer. The project’s focus is developing data science go-to-market solutions for some of EY’s target industries, aiming to build AI-based assets intended for wider commercial application.

EY is a leader in assurance, tax, transaction, and advisory services, harnessing data analytics and artificial intelligence to offer innovative consulting solutions. EY’s focus on industry-specific solutions is meant to generate broad economic benefits, improving market competitiveness and efficiency across sectors beyond those targeted in this project.

The SUDS EY collaboration showcases EY’s commitment to innovation through data science and also serves as a model for other companies considering similar collaborations.

“The quality and calibre of work by the SUDS scholars was outstanding,” said Shawn Sigesmund, EY Canada SAP National Practice Co-Leader.

“They hit the ground running with regards to their positive attitude and how they were able to so quickly and seamlessly fit into the EY workplace. The students immediately started adding value and contributing to delivering client work.”

Throughout the summer, the SUDS scholars applied their data science skills to EY’s project, gaining hands-on experience while simultaneously refining their technical and professional competencies. The SUDS scholars’ journey began with a data science bootcamp in May, providing them with essential technical skills, such as data analysis, machine learning, and visualization techniques. In addition to these foundational tools, they received professional development through the SUDS Cohort programming.

“The internship has greatly enriched my learning and career aspirations,” says SUDS scholar, Farah Mikati.

“The experience I gained has enhanced my skills and solidified my commitment to pursuing a successful career in the data science field.”

“I joked with everyone that the opportunity to work with EY and DSI felt like a gift from the sky,” said SUDS scholar, Minh Dang.

“It has significantly helped me grow my technical, interpersonal, and business skills in a professional environment.”

This collaboration is part of the larger DSI Mitacs initiative for Data-driven Decisions & Discovery: Innovation for Transformative Impact. This initiative highlights the growing importance of data science in industry and demonstrates the significant potential of industry-academic collaboration.

In addition to funds from MITACS, one of the key advantages of the SUDS-Mitacs partnership opportunity is the streamlined application process for fast-tracked projects. With DSI’s assistance, the application process for Mitacs funding has a remarkably fast turnaround time of just two weeks to receive project approvals after proposal submission to Mitacs. Companies benefit from this efficiency, allowing them to focus on innovation rather than administrative hurdles.

DSI also plays a critical role in finding a faculty supervisor for each Mitacs project, ensuring that interns receive the academic guidance necessary for success. These faculty connections can lead to long-term partnerships between companies and U of T researchers, opening the door to larger projects.

“This successful collaboration highlights the benefits of partnering with DSI for Mitacs projects. Companies gain access to top-tier talent through the SUDS, which offers rigorous technical and professional training,” highlights Sumaiya Hossain, DSI Partnership & Business Development Officer.

Is your company interested in leveraging data science for innovative solutions? Partnering with DSI through Mitacs is a strategic move that capitalizes on government funding to lower costs, which can yield immediate results and foster long-term growth.

Interested in learning more? Contact DSI today to explore how a SUDS-Mitacs collaboration can benefit your organization.