Client Services Lead

The ideal candidate for this position should have previous experience in building data science/algorithms based products, which would be a significant advantage. Experience in handling healthcare data is also desired. An educational qualification of Bachelors/Masters in computer science/Data Science or related subjects from a reputable institution is required. With a typical experience of 7-9 years in the industry, the candidate should have a strong background in developing data science models and solutions. The ability to quickly adapt to new programming languages, technologies, and frameworks is essential. A deep understanding of data structures and algorithms is necessary. The candidate should also have a proven track record of implementing end-to-end data science modeling projects and providing guidance and thought leadership to the team. Experience in a consulting environment with a hands-on attitude is preferred. As a Data Science Lead, the primary responsibility will be to lead a team of analysts, data scientists, and engineers to deliver end-to-end solutions for pharmaceutical clients. The candidate is expected to participate in client proposal discussions with senior stakeholders and provide technical thought leadership. Expertise in all phases of model development, including exploratory data analysis, hypothesis testing, feature creation, dimension reduction, model training, selection, validation, and deployment, is required. A deep understanding of statistical and machine learning methods such as logistic regression, SVM, decision tree, random forest, neural network, and regression is essential. Mathematical knowledge of correlation/causation, classification, recommenders, probability, stochastic processes, NLP, and their practical implementation to solve business problems is necessary. The candidate should also be able to implement ML models in an optimized and sustainable framework and gain business understanding in the healthcare domain to develop relevant analytics use cases. In terms of technical skills, the candidate should have expert-level proficiency in programming languages like Python/SQL, along with working knowledge of relational SQL and NoSQL databases such as Postgres and Redshift. Extensive knowledge of predictive and machine learning models, NLP techniques, deep learning, and unsupervised learning is required. Familiarity with data structures, pre-processing, feature engineering, sampling techniques, and statistical analysis is important. Exposure to open-source tools, cloud platforms like AWS and Azure, and AI tools like LLM models and visualization tools like Tableau and PowerBI is preferred. If you do not meet every job requirement, the company encourages candidates to apply anyway, as they are dedicated to building a diverse, inclusive, and authentic workplace. Your excitement for the role and potential fit may make you the right candidate for this position or others within the company.,