TELUS Digital AI Data Solutions

Pros: Working as a Data Scientist provides solid career growth in AI and data solutions. The team is very supportive, encouraging continuous learning. The hybrid model offers great flexibility in Vancouver, BC, and contributing to impactful telecommunications projects is rewarding.
Cons: Approval processes can be a bit slow, typical for a large enterprise. While growth is supported, promotion timelines are structured, so advancement is steady but not always rapid.
Advice to Management: Continue to streamline internal approval workflows where possible to help accelerate project delivery.

Pros: I really appreciate the collaborative team environment. My colleagues are super knowledgeable, and I've had many chances to work on interesting projects involving machine learning and data analytics. There's a strong focus on professional development, so I feel like my skills are constantly improving. It's great for anyone looking to build a career in the AI space. The hybrid work setup is also a huge plus, offering good flexibility.
Cons: Sometimes, the internal approval processes can feel a bit slow, which can delay project timelines slightly. While there are opportunities, career advancement isn't always as fast as I'd hoped, and the path to senior roles could be clearer. Just minor things that could be streamlined for a better employee experience.
Advice to Management: Focus on streamlining approval processes and creating clearer pathways for career advancement to retain top talent. Continue fostering the strong team collaboration.

Pros: I enjoy the focus on continuous learning as an AI Solutions Developer. We get to work with exciting machine learning and data science tech. The teams are collaborative, and the company culture is supportive. The hybrid work model really helps with work-life balance too, which is great.
Cons: Sometimes the internal approval processes can be a bit slow. Also, career progression, while available, can feel a little slower than I've seen at other tech companies.
Advice to Management: Focus on streamlining some of the internal approval workflows to empower teams to innovate faster. Also, clearer paths for career progression would be beneficial for those in highly specialized roles like AI development.

Pros: As an AI Engineer, I really appreciate the supportive team and the interesting data science challenges in telecom. There's good career development and management genuinely promotes work-life balance. Hybrid work is great, and job security feels strong. It's a positive environment for professional growth.
Cons: Approval processes can slow down AI projects sometimes. Also, cross-functional communication could improve to reduce silos. Overall, minor issues.
Advice to Management: Focus on streamlining some of the internal approval processes to accelerate AI project delivery. Continue to foster clear communication channels between different teams to break down silos, ensuring leadership across all units is aligned on project priorities and resource allocation.

Pros: I really appreciate the learning opportunities for a Junior Data Scientist. My team is incredibly supportive, which helps a lot with complex machine learning projects. It's a stable environment, and I generally maintain a good work-life balance, which is huge in the AI industry. I've grown my skills a lot here.
Cons: Career progression can feel a bit slow, especially for moving up. Also, some internal approval processes for new tech or projects are a bit clunky, typical for a larger company. This can sometimes delay our work.
Advice to Management: Streamline internal approval processes to accelerate project timelines and foster quicker career progression paths for high-performing individual contributors.

Pros: The base salary for a Data Scientist is competitive, especially for big tech in Vancouver. You get really solid health benefits, which is a big plus. It's good to know I'm covered.
Cons: Annual raises aren't huge, so career growth pay-wise can feel slow. The bonus structure for AI Data Solutions isn't super generous. It's tough to really get ahead financially.
Advice to Management: Look into increasing annual merit raises and make the bonus structure more rewarding. For Data Scientist roles, it's important to keep compensation competitive to retain talent.

Pros: You get to work on some decent machine learning projects. It's a big tech company, so there's a lot of infrastructure to learn from. Good for new grads as a first step in the AI data solutions space.
Cons: Career growth feels really slow here, especially for senior Data Scientist roles. There aren't many clear paths to move up unless you switch teams. Internal mobility is tough in the Vancouver office.
Advice to Management: Create clearer career progression frameworks for technical roles, especially for Data Scientists. Invest more in internal training and mentorship programs to help people move up.

Pros: I've really appreciated the work-from-home options here. As a Data Scientist, I can usually set my own schedule outside of core meetings. It's a big plus for work-life balance, especially being remote from Vancouver.
Cons: But it's not totally flexible. There are still pretty strict 'core hours' for team syncs, which can be tough. And sometimes project deadlines mean working longer days, even with the WFH setup in this corporate environment.
Advice to Management: Try to make core hours more flexible or team-specific. It would help a lot with true work-life balance for those of us working remotely across different time zones.

Pros: The work-life balance is pretty solid, usually around 40-hour weeks. The hybrid model in the Vancouver office is a big plus; it offers good work flexibility. You get to work on some interesting AI projects too.
Cons: Career growth as a Data Scientist is tough. There aren't many opportunities for promotion, and skill development feels limited for machine learning roles. You often feel stuck in your current level.
Advice to Management: Invest more in clear career paths and skill development programs, especially for data and AI roles. Promote from within more often.

Pros: My direct manager was usually solid, especially for a Data Scientist navigating complex machine learning models. They really help you grow. There's good support for learning new tech within AI Data Solutions projects.
Cons: The biggest issue is inconsistent leadership across different departments, it's tough to get everyone on the same page. It sometimes feels like upper management in this corporate environment makes decisions without much real-world input. Career growth for Data Scientist roles isn't always clear, even in our hybrid work model in Vancouver.
Advice to Management: Work on aligning leadership's vision and communication across all the AI Data Solutions teams. Create clearer growth paths for data scientists and other technical roles.