Liberty IT’s Lara Hawchar discusses her role in the data science community and her advice for other professionals.
Lara Hawchar is a principal data scientist at Liberty IT, where she specialises in MLOps and the advancement of data science best practices. A role she explained has enabled her to empower the data science community and focus on predictive modelling, GenAI applications and driving improvements in standards.
Recently announced as one of Liberty IT’s first Culture Stars, as part of a peer-selected recognition programme, Hawcher strives to “lead with empathy, think creatively and uplift others”, while contributing her insights to the thriving tech sector.
“This was honestly a really special moment for me,” Hawchar told SiliconRepublic.com. “The Culture Star award is an open nomination across Liberty IT so it really means a lot to be recognised by my colleagues. It’s a good reminder that what I do makes a difference and is appreciated.”
How would you describe a typical day at work?
Typically, I start my day at 9am by checking my emails and Teams messages that were sent the previous evening from our peers in the US, in case I need to catch up on anything. I then have a daily stand-up with my team to discuss any progress, issues or updates. Then I will prioritise my tasks for the day. These could be anything from hands-on coding, reviewing codes, to prepping presentations for demos or stakeholder meetings – every day can bring something new. The afternoons are typically busy with meetings as our colleagues in the US get online. In between meetings, the developer work continues until I finish up for the day.
As a principal data scientist, how does your role feed into the wider sector?
My role goes beyond working on a single product or project to also helping other data scientists do their best work. I spend a lot of time connecting teams, sharing what’s working and what isn’t, and making sure we’re all heading in the right direction together. That can mean suggesting new tools, some automation solutions, promoting best practices or helping design solutions that actually solve the problems our teams face. I also try to bring teams together and encourage collaboration, so we can learn from each other and adopt best practices across the board.
What types of projects are you currently working on?
Currently, multiple teams across my space are independently experimenting with generative AI (GenAI) solutions, especially in data extraction projects. While this encourages innovation, it can also lead to duplicated efforts, inconsistent standards and slower delivery.
I’m establishing a cross-functional working group that unites teams working on GenAI initiatives across our space. This group will coordinate activities, promote knowledge sharing and upskilling, and develop best practices to create high-performing teams.
Personally, I find this fulfilling because I enjoy working with cross-functional teams, helping shape best practices and promoting them to my peers.
You talk about empowering the data science community, in what ways can this be achieved?
Empowering the data science community is something I care deeply about in my role as a principal data scientist. I try to help foster collaboration and encourage knowledge sharing across teams. I do my best to support and mentor others, helping them stay up to date with the latest data science tools and methods. I’ve found that regular stand-ups, knowledge sharing and demo sessions are simple but effective ways to reach more people and keep everyone connected.
Being an MLOps ambassador gives me another opportunity to connect with others in the community and help promote best practices where I can.
Overall, I just try to create an environment where everyone feels encouraged to learn and grow together.
What skills do you use on a daily basis?
Every day I lean on a mix of technical and non-technical skills. On the technical side, I’m often coding – usually in Python – or reviewing code, working with data and thinking about how to improve our workflows. But I’ve found that some of the most important skills aren’t technical at all. Communication is huge. I spend a lot of time talking with different teams, explaining ideas in plain language, and bringing people together to solve problems.
One skill people might not expect is relationship building. A big part of my job is connecting different groups, whether it’s data scientists, engineers or stakeholders from other departments, and making sure we’re collaborating effectively. Honestly, sometimes just being a good listener or showing empathy goes a long way, especially when we’re facing challenges as a team.
So yes, I use coding and data science skills daily, but things like communication and building trust are just as important – sometimes more.
What are the hardest parts of your working day and how do you navigate them?
One of the hardest parts of my day is managing a very busy calendar. As a principal data scientist, there are always meetings popping up: stand-ups, cross-team syncs, stakeholder updates and especially the afternoon sessions when colleagues in different time zones come online. On top of regular project work, I often help with interviewing candidates, prepping presentations or demos for leadership, and getting ready for leaders’ visits. Finding time for focused work – like coding, reviewing solutions or deep problem-solving – can sometimes be challenging. What helps me with this is setting aside blocks of time on my calendar for dedicated ‘heads-down’ work and being realistic about how much I can achieve in a day.
Are there any productivity strategies or tips that you’ve found particularly helpful?
One thing that’s helped me a lot is making use of the times between meetings. If I have 30 minutes free, I’ll tackle quick tasks – like replying to emails, answering technical questions on Teams, reviewing a short document or cleaning up a script. Time-boxing these little tasks means I don’t end up dragging them out or letting them pile up, and it’s always satisfying to tick something off the ‘To Do’ list.
For the bigger, heads-down work like coding or prepping for a demo, I try to block out longer chunks of time – usually in the morning when my calendar is less packed. Protecting that focus time is key, especially with all the responsibilities that can pop up throughout the day.
How has your role changed as the sector has grown and evolved?
The biggest shift recently has definitely been the rise of GenAI and tools like Copilot. The data science landscape is changing fast, and I think we’re all learning together as these technologies develop. There’s a lot of excitement, but we also want to prove value quickly – everyone wants to see results and start using AI-powered solutions right away.
Now there’s a real need to build a strong data science community that understands how to leverage GenAI day to day and deliver reliable, high-performing tools. My role has evolved to focus much more on bringing people together, sharing learnings and building best practices, rather than just working on one project at a time.
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