The hottest career path for the twenty-first century has frequently been referred to as data science. How does a data scientist spend their day, though?
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Sadaf Sayyad, a data scientist at Intuit, spoke with Analytics India Magazine (AIM) about a typical workday, interesting incidents, career development, and the value she brings to the team and ecosystem.
The stage of the project one is working on determines the typical day for a data scientist. On the whole, though, I start each day by reviewing my emails and messages for any urgent tasks. The next step is to plan my day after which we have a stand-up meeting to talk about the project’s status and any roadblocks, according to Sayyad.
At the Indian Institute of Science (IISc), Bengaluru, Sayyad earned a master’s degree in management. Analytics, data science, and machine learning were the main topics of her elective courses. She then received a campus placement at Walmart, where she had the opportunity to work on optimization and machine learning projects. She then began working for LinkedIn, where she was a member of the data science team and was in charge of conducting in-depth research and testing on the features of LinkedIn’s jobs pages. I added more knowledge of important business metrics, stakeholder management, product ownership, and the power of data insights to influence business decisions during this time, the employee continued.
In an interview with AIM, Sayyad claimed that if she weren’t a data scientist, she would have worked as a quantitative financial analyst. Sayyad stated, “I became interested in finance during an internship at a hedge fund and would have pursued it further if I weren’t a data scientist.
hurdles and hoops
Being a data scientist is both challenging and beautiful because every problem you encounter is probably unique. The single approach will therefore probably not work both problems. The fact that every project presents a fresh opportunity for learning makes our work extremely exciting, according to Sayyad.
She went on to explain that, at a high level, the procedures to be followed are similar, including defining a problem statement and setting clear expectations, making sure we have the right kind and quality of data, and establishing success metrics. “To ensure whether a project is worthwhile, we build the first version model or solution as a proof of concept. The benefit of building and maintaining a model would then seem to outweigh the cost if the target metrics appear to be favorable. In that case, we proceed to build a production-level model, Sayyad added.
Breaking through data science block
A common problem for data scientists is that they are overworked or under pressure, which can cause data science blocks that can interfere with their daily activities. Sayyad claimed that she overcame this obstacle through teamwork, though.
“This depends on the reason for the block. When data is a problem, we work with others to find alternative data sources. If that doesn’t work, we consult with business stakeholders to determine the best strategy and the best results we can achieve given the data and resources at our disposal. The second roadblock might occur if one hits a model accuracy that doesn’t seem to get better despite trying different approaches, Sayyad explained.
Getting a new perspective and using team knowledge, according to her, may be helpful at this point. Furthermore, getting back on track can be accomplished by discussing one’s approach with other data scientists and exploring new possibilities.
Work-related motivation
The biggest motivator, according to Sayyad, is the knowledge that she is working on a product that makes a meaningful difference in people’s lives by fostering small businesses’ and consumers’ prosperity. She added that one of the best motivators for her as a data scientist is the chance to work on interesting problems and learn something new every day.
Aims for the future
According to Sayyad, she wants to advance her technical knowledge of artificial intelligence, grow current on current research, and contribute to the community of AI experts.
“I want to keep using the power of AI to improve people’s lives. I couldn’t be better paced with Intuit’s ‘AI-driven expert platform’ strategy, said Sayyad.
Work for Intuit
Sayyad explained, “At Intuit, my role has changed from building ML models focusing on the technical aspects and algorithms to extending this to building reusable AI-based systems focusing on improving customer experience and ease of use.”
She claimed that while working at Intuit, she had numerous opportunities to work on some incredible and significant projects, providing the business with key success metrics and learning and implementing cutting-edge ML techniques that had aided in her professional development.
Sayyad stated, “I am currently working on a project which will help us improve customer experience significantly as they provide resolution to the problem they raise, using the power of AI. For this project, she claimed to use natural language processing (document classification and named entity recognition techniques) in addition to computer vision (optical character recognition).
She has experience with supervised machine learning issues as well as multivariate anomaly detection.
Culture at Work
“It is an honor for me to work with such a talented team of people at Intuit, where we constantly learn from one another. Sayyad said, “It’s not an exaggeration to say that everyone personifies the company’s value of “Stronger Together.”
She added that the leadership is very clear about the top-level objectives known as “Big Bets” and the tech priorities, and every project is aligned to these objectives, ensuring that they constantly have their eyes on the big picture and what they are working towards.
Additionally, she noted that Intuit’s employee-first and empathy-driven policies have led to the company consistently ranking among the top three best places to work in the Great Place to Work ranking.
At the moment, Intuit’s AI and data science team has about 500 members, who are dispersed across various locations. For instance, the team in India consists of business analysts, program managers, machine learning engineers, and engineers for the infrastructure of machine learning.
According to Sayyad, peer-to-peer learning is highly encouraged and has many opportunities. We regularly hold knowledge-sharing meetings as a team and have resources at our disposal to learn out what other team members have been working on. One way we contribute each other succeed is by participating in these forums and offering insightful feedback,” the speaker added.
We are committed to giving everyone the opportunity to succeed here at Intuit. We’re always looking for talented individuals to join our team at Intuit if you’re looking for a way to develop cutting-edge solutions while also benefiting the world. Visit our careers website to learn out more and to apply for available positions.
