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Founded in 2011, New York-based IT services and consulting firm Marlabs helps companies of various sizes to undergo AI-powered digital transformation. It provides a wide range of services, including strategic planning, creating rapid prototypes in specialised labs, and applying agile engineering techniques to develop and expand digital solutions, cloud-based applications and AI-driven platforms.
Marlabs’s data science team addresses a range of industry challenges, emphasising tasks like extracting insights from extensive datasets and employing pattern recognition, prediction, forecasting, recommendation, optimisation, and classification.
Exploring Generative AI at Marlabs
“In operationalising AI/ML, we have tackled diverse projects, such as demand forecasting, inventory optimisation, point of sale data linkage, admissions candidate evaluation, real-time anomaly detection, and clinical trial report anomaly detection,” Sriraman Raghunathan, digital innovation and strategy principal, Marlabs, told AIM in an exclusive interaction.
The team is also exploring generative AI applications, particularly in knowledge base extraction and summarisation across domains like IT service desk ticket resolution, sustainability finance, medical devices service management, and rare disease education.
However, it is not developing foundational models as of now due to substantial capital requirements. “Instead, we are focussing on the value chain beyond foundational models, offering tools and practices for deploying such models within organisation boundaries, tailored for specific domains,” he added.
Marlabs employs a variety of tools and frameworks depending on project specifics, utilising R and Python for development, Tableau, Power BI, QlikView for data exploration and visualisation, and PyTorch, TensorFlow, Cloud-Native tools/platforms, and Jupyter Notebooks for AI/ML model development.
The team leverages Transformer models like GPT-3, especially in NLP use cases, implementing them in TensorFlow, and PyTorch, and utilising pre-trained models from Hugging Face Transformers Library. For generative AI, their toolkit includes LangChain, Llama Index; OpenAI, Cohere, PaLM2, Dolly; Chroma, and Atlas.
Hiring Process
The hiring process for data science roles at the organisation emphasises a blend of technical knowledge, practical application, and relevant experience. The initial steps involve a clear definition of the role and its requirements, followed by the creation of a detailed job description.
The interview process comprises technical assessments, video interviews with AI/ML experts, and HR interviews. Technical assessments evaluate coding skills, data analysis, and problem-solving abilities.
Video interviews focus on the candidate’s depth of knowledge, practical application, and communication skills, often including a discussion of a relevant case study or project. HR interviews center around cultural fit, interpersonal skills, collaboration, and the candidate’s approach to handling challenges.
Expectations
“Upon joining the data science team, candidates can anticipate a thorough onboarding process tailored to their specific team, providing access to essential tools, resources, and training for a smooth transition,” commented Raghunathan.
The company’s AI/ML projects involve cutting-edge technologies, exposing candidates to dynamic customer use cases spanning natural language processing, computer vision, recommendation systems, and predictive analytics. The work environment is agile and fast-paced. The company places a strong emphasis on team collaboration and effective communication, given the collaborative nature of data science and AI/ML projects.
In this rapidly evolving field, the company expects new hires to demonstrate continuous learning, tackle complex technical and functional challenges, operate with high levels of abstraction, and exhibit creative and innovative thinking.
Mistakes to Avoid
“The most prevalent error observed in candidates during data science role interviews is a lack of clear communication,” he added.
The ability to effectively communicate insights to non-technical stakeholders is crucial in the AI/ML space, and this skill is frequently overlooked.
Another common mistake is a failure to comprehend and articulate the business context and domain knowledge of the problem, which is essential in AI/ML applications with significant business impact.
Work Culture
“We are recognised for our value-based culture focused on outcomes, emphasising a flat organizational structure to spur innovation and personal growth. Key values such as respect, transparency, trust, and a commitment to continuous learning are central to their ethos, all aimed at exceeding customer expectations,” he said.
The company’s robust learning and development program has prepared over 150 young managers for leadership roles, with a strong emphasis on AI and technology for organisational insights and sentiment analysis.
The company offers a comprehensive benefits package, including versatile insurance plans, performance incentives, and access to extensive learning resources like Courseware and Udemy, supporting a hybrid work model. Additionally, they provide mental health support and reward long-term employees based on tenure.
Raghunathan further explained that in the data science team, Marlabs stands out for its innovative and collaborative environment, encouraging creativity and continuous learning. “This distinctive culture and investment in employee growth make us a leader in data science, differentiating it from competitors in the tech industry,” he added.
Why Should You Join Marlabs?
“Join Marlabs for a dynamic opportunity to work with a passionate team, using data to drive meaningful change. In this collaborative setting, data scientists work with brilliant colleagues across various industries, including healthcare, finance, and retail. You’ll tackle complex issues, contributing to significant business transformations. Marlabs supports your career with essential tools, resources, training, competitive compensation, benefits, and opportunities for professional growth and development,” concluded Raghunathan.
