Access to the Internet has led to an explosion in the amount of data being created leading to exponential growth in analytics and Artificial Intelligence. Consumers today want smarter recommendations, intelligent products and precise predictions. As a result Data Science is rapidly gaining popularity as a career choice.
Constant innovation
Innovation forms the bedrock of data science. From recognising the potential for a smart solution to identifying data requirements to channelling existing sources to designing and developing this solution, data science is all innovation. On the one hand, there are organisations that build AI/ML-based products to solve a specific, predetermined problem, like the Roomba robotic vacuum that simplifies vacuuming. On the other are those that provide services to make businesses more profitable and satisfy end customers like enabling every store of a global grocery chain to predict when their last carton of milk will fly off their shelf and plan their supply chain and procurement operations efficiently.
Unlike the former, which results in AI/ML-based products demonstrating human-like intelligence in machines (voice-controlled AI assistants, driverless cars, autonomous robots for surgical precision), the latter is typically implemented on cloud-based servers within the business environments. These solutions could be conversational AI interfaces, chatbots, image-recognition platforms to detect products, faces, vehicles, or defects, or even language translation platforms.
In transforming data into “actionable” products or insights, innovation is required to go beyond the status quo. One approach is to identify a better alternative to an existing solution. The other involves redefining the entire framework and building a more meaningful, efficient solution.
There are multiple roles within data science that provide the most opportunity for innovation. Data Scientists specialising in AI and ML are constantly pushed to explore new ways of using these technologies to solve problems. ML Engineers are adept in productionising the models built by data scientists, and incorporating the infrastructure for model and data drift management, containerisation, deployment, and monitoring model performance. Data engineers innovate to develop new data storage and management methods and create the most efficient pipelines and data warehousing systems. Data Analysts explore multiple new methods to dive deep into the data and extract actionable insights for the business by creating innovative reports and dashboards that provide real-time global updates. Developers seek new ways to use data to improve user experience. In each role, it is essential to experiment and take risks to find new and better ways of doing things.
Selecting the company
To identify if an organisation is a good fit, ask about solutions, the industries they focus on, the research and development balance, and the emphasis on learning and development initiatives. Candidates should also ask about the company’s process of generating new ideas, how they implement change, and whether employees are encouraged to be creative and think outside the box. Not only will this set you apart but also give you a better sense of the company’s innovation and whether you’ll have the opportunity to contribute.
Opportunities for innovation can be found both in start-ups and big tech. Ultimately, students should follow their passion and pursue the type of company they believe will provide the best opportunity for them to succeed.
The writer is Manager and Lead Data Scientist- Tredence
Published - December 18, 2022 08:51 pm IST
