Sergei Batishchev recently joined Mesh-AI as Lead Data Scientist.
We sat down with him to chat about his background, what interested him in working with data, AI and ML and what led him to Mesh-AI.
My journey into data started when I joined IBM as a graduate on their data science track. Coming from a physics background, I had an understanding of statistical processes along with a foundation in Python, which I quickly learned was a valuable toolset in the world of business.
Working in a fast-paced consultancy has given me a broad overview of the technology landscape from source to serve, and I continue to be a keen student of the evolving market.
I hypothesised that the growing scale of data would present ever-evolving opportunities, which would (in theory) keep the domain fresh even as I gained more experience in it. I have to say that it didn’t disappoint!
Also, working as a consultant would mean that there would be a blend between both technology and people in my day-to-day role which appealed to me.
My job is to come up with and implement AI-based solutions to problems that the businesses we work with have.
This involves working closely with both product owners and data engineers to qualify both the value and feasibility of various solutions. We then take those solutions forward into products to bring value to our customers: be that in cost savings, increased efficiency/productivity, improved decision making, etc.
I joined Mesh-AI to be able to have a real impact on the businesses we work with. I was interested in the challenge of coming up with and delivering a solution end-to-end.
I’m also interested in the fact that Mesh-AI is both an early stage start-up and a consultancy. Joining so early I’m excited to contribute to a company which is growing at breakneck speed!
This is a difficult question to answer and every company has its own take. I think the goal of a great company culture is to bring out the best in its employees, which in turn brings out the best in the business.
For me, what matters most is a ‘we’re in this together’ attitude where people help each other succeed at all times. I think it’s also important to work somewhere where people take the initiative to identify and solve problems that materially contribute to the business, even if they are not strictly within their job description.
Every enterprise is at a different stage of maturity with respect to their data.
On a fundamental level, large enterprises have huge swathes of data that are often semi-locked away behind products bought to service specific (and unrelated) problems. This in turn creates a landscape where data is fragmented and not easily worked with. Since much of the power of AI/ML comes from bringing together rich data sources, enterprises should be focusing on decreasing friction here whilst also investing in data scientists to come up with creative solutions to the challenges an enterprise may have.
There is also a need to invest in the management of productionised models, especially as the number of models in production grows. The currently accepted MLOps lifecycle found by doing a quick Google will explain how to take a single use case to production, however the ongoing support of many models is becoming a headache for many organisations.
If you’re interested in joining the Mesh-AI team - we’re hiring! Get in touch with us at email@example.com or find our open roles here.
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