In September last year, global advertising and marketing tech firm Epsilon introduced Epsilon AI Audiences, a new offering to identify potential consumers ready for purchases or donations. Unlike traditional marketing, it offers three smart solutions for personalised, people-centred marketing.

“At Epsilon, we focus on how consumers see the impact of their interactions with brands. We believe that combining predictive AI with generative AI can yield the best outcomes for both brands and consumers,” Lakshmana Gnanapragasam, senior vice president of strategy and analytics at Epsilon, told AIM in a recent conversation.

Currently, the Texas-based company is working on various use cases for generative AI, including real-time summarisation of marketing and loyalty campaign performance with actionable recommendations. It facilitates natural language-based ad-hoc queries on customer, product, store, and campaign data, generating audience lists for media activation based on marketing objectives and budget considerations. 

It’s also developing hyper-personalised creative assets for marketing and media campaigns.

Epsilon also operates an Analytics Center of Excellence (A-CoE) in India, focusing on associate learning, development, and career progression. Gnanapragasam further shared with us the company’s data science hiring process and more. 

The company has multiple open positions across all levels, from senior data scientists to managerial roles, at its Bengaluru office.

Inside Epsilon’s Data Science Team

“The primary problem addressed using data science revolves around helping brands understand and effectively engage with their customers throughout their lifecycle,” Gnanapragasam added.

The engineering team consists of over 2,000 associates, and the data science team has over 400 associates globally. Engineering teams are organised according to the platforms they are working on, such as technology, media, and data platforms. On the other hand, data science teams are structured by business units and further divided into product analytics and client analytics teams to embed AI/ML features and demonstrate platform value to clients, respectively. 

Leveraging extensive consumer data covering demographics, lifestyle, purchases, media consumption, and online behaviour, Epsilon helps brands in various aspects. These include, customer acquisition, retention, reactivation, cross-selling, upselling, product recommendations, real-time engagement, media planning, execution, measurement, attribution, and loyalty.

Implementing AI and ML enhance Epsilon’s operations, offering solutions like people-based identity verification, behaviour and opportunity segmentations, next-best-action and product recommendation algorithms, and real-time personalisation in digital media solutions. 

Tech Stack

Gnanapragasam detailed the company’s use of cloud-based SaaS applications for their customer data platforms, operating on cloud platforms such as AWS, Azure, and GCP, and incorporating Databricks and SageMaker applications. 

The technology stack encompasses Python, Spark, PySpark, R, SQL, and SAS, tailored to specific use cases, platforms, and client technical requirements. 

They leverage diverse technological capabilities, including Apache Spark for distributed data processing, Amazon S3 for scalable storage, Python, PySpark, and SQL for data analysis. Besides, there’s Sklearn and MLlib for model development, TensorFlow, Keras, and PyTorch for deep learning, Databricks and SageMaker for comprehensive platform support, and visualisation tools like Tableau and Power BI for reporting. 

Additionally, transformer-based open-source models from Hugging Face are employed for generative AI applications alongside open-source frameworks such as LangChain and LlamaIndex.

Hiring Process

The prospective candidates undergo a series of assessments before being hired. Initially, they attend an HR round to qualify for the job opening. Following this, all candidates take a proctored technical test and participate in one or two in-depth technical interviews to evaluate their technical knowledge, problem-solving abilities, and practical skills. 

After successfully completing these stages, candidates undergo a hiring manager interview, followed by the HR round. This final phase is crucial for assessing alignment with company values and cultural fit. It provides a comprehensive evaluation beyond skills and qualifications to determine how well candidates would integrate into the team. 

For mid- to senior-level hires, the process may include solving an analytical challenge through a coding exercise, extending the evaluation further.

“The primary need for entry-level positions is proficiency in Python. We require experience with PySpark SQL, SAS, and machine learning for mid-level positions.

For senior-level candidates, we look for technical depth across multiple ML platforms and strong product or client orientation, depending on the team they join,” Gnanapragasam added. 

However, he noted that candidates should thoroughly research the company and the job requirements when interviewing for a data science role at Epsilon. 

To improve their chances of getting hired, candidates should ensure they have a solid understanding of Epsilon, its products, clients, and the nature of the work. They should tailor their resumes to highlight experience with customer data and improve marketing outcomes. 

Expectations

When candidates join the data science team, they can anticipate working on enhancing platform features using analytics and machine learning or optimising marketing and loyalty outcomes through data analysis. 

“As I said earlier, our rich consumer data assets, combined with the client’s first-party data in a secured, privacy-safe environment, provide one of the best playgrounds for data scientists to continually learn and improve their analytics skills,” he added. 

The company facilitates work by providing comprehensive data analytics toolkits and accelerators on its tech platforms. Additionally, the platforms support campaign measurement and attribution, enabling data scientists to evaluate the effectiveness of their recommendations. 

“We expect associates to be curious and apply critical thinking skills when working on our platforms or client solutions. As an organisation, we believe good ideas and great execution can come from any place, and capability has nothing to do with age,” commented Gnanapragasam. 

Work Culture

“We are a people-first organisation. We value employee wellbeing, which is the centre of all our policies,” Gnanapragasam added. 

Through programs like EPIC, the company fosters empathy and collaboration, both internally and within the communities it serves. Employees enjoy inclusive insurance policies and access to wellness platforms like HealthifyMe and Headspace. Learning and development initiatives are personalised to address individual needs and cover a wide range of topics, including behavioural training and diversity. 

Additionally, a hybrid work model promotes teamwork and collaboration, with most associates in the office two days a week and flexible remote work options on the remaining days.

Check out the careers page now.

Read more: Data Science Hiring Process at Confluent