Minnesota-headquartered retail giant Target Corp made its foray into Bengaluru almost two decades ago in 2005. Since then, Target in India (TII) has functioned as an integrated global capability centre for the company.
It serves as an extended headquarters for Target US, bringing the data sciences team closer to decision-making processes. This is crucial in driving the company’s business priorities across various verticals, including digital search and advertising technology, loyalty and marketing, merchandising, supply chain logistics, and forecasting.
“Our data science team is responsible for building, governing, and maximising the use of Target’s data assets for better decision-making across our retail operations” Likitha Nanda, the director of talent acquisition at TII, told AIM earlier this week in an exclusive interview.
The team comprises over 130 specialists from various domains, including statistics, operations research, natural language processing, vision computing, economics, computer science, and MLOps.
“We’ve been using AI for many years to support our team and drive speed and efficiency across all our operations — everything from our supply chain and inventory management systems all the way to applications in the checkout lines in our stores and on our website and app,” Nanda added.
AI is one of the cornerstones of how the company creates guest experiences by personalising interactions and ensuring that favourite products are consistently available both in stores and online. It has developed a suite of tools called ThinkTank to host and manage generative AI use cases.
Currently, TII is hiring for over 40 data science roles across different departments in search, adtech, supply chain and more.
Inside Target’s AI and Analytics Play
According to Nanda, one of the primary solutions developed by the DS team involves integrating the proprietary ‘Guest Data Platform’ with data science to personalise the shopping experience. Previously, different business units had fragmented views of guest behaviour, making it challenging to identify comprehensive shopping patterns.
The Guest Data Platform now claims to aggregate all guest data into a single source, providing a cohesive view of shopping behaviours. This allows Target to personalise shopping experiences, anticipate customer needs, and deliver tailored interactions in-store, online, or through the app.
“The impact of data sciences-driven products has collectively delivered incremental value to Target and contributed to profitability, while also creating cost efficiencies through innovative and scalable AI solutions,” she explained.
The team also works on building ML and statistical models to predict and forecast vital decision points for planning and operations. This includes purchasing and replenishment systems, optimisation models for inventory placement, pricing algorithms, and demand forecasting models.
Other models are prescriptive, integrating directly into digital and marketing experiences like search personalisation, loyalty programs, and AdTech, enhancing the overall guest experience.
Tech Stack
Besides, Target’s data science team leverages pre-trained models and proprietary data to develop tailored solutions.
For modelling, the team utilises Python, Spark, R, and Kotlin, with data managed via Kafka topics and APIs integrated with execution systems. Training models are supported by large-scale on-premise compute infrastructure using CPUs and GPUs, with data interchange handled through batch or streaming.
Additionally, an MLOps platform is used to train, deploy, and maintain data science models, enabling scalable deployment. On the other side, Transformer-based models, including GPTs, are leveraged for various guest experiences and digital use cases.
Hiring Process
The hiring process for data science roles at TII begins with a recruiter screening candidates for role fit, work experience, and qualifications. Following this, candidates have a brief meeting with the hiring manager. The interview process includes three rounds: a technical interview, a statistical interview, and a final meeting with the hiring manager. Although not mandatory, the company prefers candidates from top institutions like IITs and IISc.
“We look for strong fundamentals and domain-specific knowledge in data science and the retail industry,” said Nanda.
Interview Rounds
Nanda told AIM that the initial meeting with the hiring manager is an information-gathering session to evaluate the candidate’s overall experience. The first technical round involves a deep dive into the candidate’s understanding of statistical modelling, AI, ML, and coding, with an emphasis on problem-solving skills.
Then, candidates are given a problem statement and must produce a clear and executable code. The statistical round further examines the candidate’s knowledge of statistical techniques, building on insights from the technical round.
The final interview with the hiring manager focuses on the cultural fit, assessing the candidate’s alignment with Target’s values of collaboration, teamwork, and a philosophy of caring, growing, and winning together.
But as Nanda suggests, while you are interviewing, make sure you avoid common mistakes like not having a deep understanding of Target’s operations and failing to clearly articulate how your skills can effectively address the company’s retail challenges.
Diversity and Inclusion
At TII, diversity, equity, and inclusion (DE&I) are integral at every step, from hiring to providing a supportive work environment and enabling career growth.
“Women comprise 46% of our workforce in India, which is much higher than the industry average” Nanda added. The aim is to ensure an inclusive work environment where all team members, including those with disabilities, feel a sense of belonging, respect, and support to do their best work.
Work Culture
“We foster a culture of continuous learning and development, allowing team members to dedicate one day a week to learning new skills and staying ahead in their professional journeys. Learning is a critical part of our team members’ growth and drives their best work,” Nanda added.
With the adoption of a hybrid work model focused on flexibility and equity, TII’s work culture is centred on caring, growing, and winning together.
The company offers inclusive benefits that cater to a multigenerational workforce with diverse needs, covering mental, physical, and financial aspects. Benefits are regularly benchmarked through internal surveys and external sources to ensure they meet the needs of its diverse workforce.
“One of the biggest factors of our differentiation is the way we function – our effective prioritisation and intake process, and engagement with cross-functional teams to deliver superior solutions,” explained Nanda.
The company has been recognised for its culture and practices as one of the 50 best firms for data scientists to work by AIM Media House in 2024.
Additionally, it has been recognised by Great Place to Work for three consecutive years, a Champion of Inclusion in the Most Inclusive Companies Index for 2023, among the Top 100 Best Companies for Women in India for six years, and its data sciences team won the Minsky Awards for Excellence in AI in 2023.
“Innovation and inspiration are at the centre of everything we do, and we encourage candidates to have an innovation-driven mindset,” Nanda concluded.
Check out the careers page here.
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