Impacting people’s lives through the power of AI

Time and time again, data science has been touted as the most promising career option of the 21st century. But do you know what happens in the life of a data scientist?

To understand this, Analytics India Magazine reached out to Sadaf Sayyad, Data Scientist at Intuit, who walked us through a typical day at his job, while sharing interesting examples, career growth and the impact it adds to the business. team and the ecosystem. .

“For a data scientist, a typical day depends on the phase of the project you are working on. But, at a high level, my day starts with checking emails and messages for any urgent tasks. Then we have a stand-up meeting to discuss the progress of the project and the blockers, followed by planning my day,” Sayyad said.

Explaining in more detail, she said that the tasks include researching data sources, cleaning data, performing exploratory analysis, designing the ML-based system, which includes inputs, outputs, measurements of success, building and experimenting with different ML models to optimize target metrics, design and conduct experiments to prove model performance in production, present model results and information to business stakeholders and collaborate with machine learning engineers for model deployment.

“Depending on what phase we are in, my day involves one or more of these tasks. Outside of work, I dedicate time to reading research papers, updating on the latest developments, peer-to-peer learning through lunch-and-learn sessions and conferences. We also have fun on team outings, games and even online team games in work from home,” Sayyad said.

Sayyad earned a Masters in Management from the Indian Institute of Science (IISc), Bangalore. His electives focused on analytics, data science, and machine learning. Post this; she got an internship on the Walmart campus, where she had the opportunity to work on optimization and machine learning projects. After that, she worked at LinkedIn in the data science team, where she was responsible for in-depth analysis and feature experimentation on LinkedIn’s job pages. “It was a stint where I learned more about key business metrics, stakeholder management, product ownership, and the power of data insights to drive business decisions,” she added.

Sayyad told AIM that she would have been a quantitative financial analyst if she hadn’t been a data scientist. “I developed an interest in finance while interning at a hedge fund and would have pursued it if I hadn’t been a data scientist,” Sayyad said.

Hoops and hurdles

“The challenge and beauty of being a data scientist is that every problem you encounter is likely to be different. Therefore, the one-size-fits-all approach probably won’t work for two problems. This makes our job very exciting because every project is a new learning opportunity,” Sayyad said.

Elaborating further, she said that at a high level the steps or processes are similar – i.e. defining a problem statement and setting clear expectations, making sure we have the right quality and good quality of data and define measures of success. “We build the first version of the model/solution as a proof of concept to ensure there is merit in pursuing a project. Next, suppose the target metrics look positive and the cost of building and maintaining a model is worth it. In this case, we are moving forward to create a production-level model,” Sayyad added.

Overcoming the Data Science Block

Often, data scientists are under pressure or overloaded with work/tasks leading to data science blocks, which could hamper their day-to-day activities. However, Sayyad said she overcomes this with team spirit.

“It depends on the cause of the blockage. Sometimes the blocking is due to a lack of data; in this case, we communicate with others to find alternative data sources, and if not, discuss with business stakeholders the best approach we can use and what we can do the best with the data and resources available. The other blockage could be when one is struck by the accuracy of a model, which does not seem to improve even after several approaches,” Sayyad explained.

She said that’s when it might be useful to have a new perspective and knowing the team helps. And talking to other data scientists about the approach we’ve taken and new things that could be tried can get us back on track.

Motivation at work

“Knowing that I’m working on a product that impacts people’s lives in meaningful ways by fueling small business and customer prosperity is without a doubt the biggest motivator,” Sayyad said. Plus, as a data scientist, she said solving exciting problems and learning something new every day is a great motivator.

career goals

Sayyad said she wants to develop her technical expertise in artificial intelligence, keep abreast of ongoing research, and contribute and give back to the AI ​​community.

“I want to continue impacting people’s lives through the power of AI. With Intuit’s strategy being an ‘expert AI-powered platform’, I couldn’t be faster,” Sayyad said.

Work at Intuit

“At Intuit, my role has evolved from building ML models focused on technical aspects and algorithms to extending to building reusable AI-based systems focused on improving the customer experience and making it easier of use,” Sayyad said.

She said she had multiple opportunities to work on amazing and impactful projects at Intuit, providing key success metrics for the business and learning and implementing cutting-edge ML techniques that helped her. helped grow as a technologist.

“I am currently working on a project that will help us improve the customer experience significantly as they provide a solution to the problem they are raising, using the power of AI,” Sayyad said. She said she is using computer vision (optical character recognition) and natural language processing (document classification and named entity recognition techniques) for this project.

Previously, she also worked on multivariate anomaly detection and supervised machine learning problems.

work culture

“At Intuit, I am delighted to work with a team of very talented people where we learn from each other every day. It’s no exaggeration when I say that everyone personifies the corporate value of “Stronger Together,” Sayyad said.

Additionally, she said that management is also very clear on high-level goals called “Big Bets” and technology priorities, and that every project is aligned with those goals, so they always have an eye on the big picture and what they are working on. towards.

On top of that, she said Intuit has repeatedly been among the top three best places to work in the Great Place to Work rankings because of its employee-centric and empathy policies.

Intuit’s artificial intelligence and data science team currently has approximately 500 members, spread across multiple geographic locations. For example, the team in India consists of data scientists, machine learning engineers, machine learning infrastructure engineers, business analysts, and program managers.

Sayyad said there was lots of encouragement and opportunities for peer-to-peer learning. “We have a cadence of knowledge sharing sessions within our team and have resources available to learn what other members have been working on. Contributing to these forums and sharing valuable feedback is one of the ways we contribute to the everyone’s success,” she added.

Sean N. Ayres