Job
Description
What You'll Do Deep analytics-tech expertise:Develop and implement advanced algorithms that solve complex business problems in a computationally efficient and statistically effective manner, leveraging tools like PySpark, Python, SQL on client/ZS cloud environments. Execute statistical and data modeling techniques (e.g., hypothesis testing, A/B Testing setup, marketing impact analytics, statistical validity etc.) on large datasets to identify trends, figures, and other relevant information with scalable and operational process implementations. Evaluate emerging datasets and technologies that may contribute to our analytical platform, including a good understanding of Generative AI capabilities and SaaS products. Communication, collaboration, unstructured problem solving, and client engagement (in a high-performing and high-intensity team environment):Problem solving and Client engagement: Understand client business priorities, develop product use cases, perform proforma analysis for estimating business opportunity, and deploy the use case for the clients. Collaboration: Work in a cross-functional team environment to lead client engagement and collaborate on holistic solutions comprising best practices from Frontend and Backend engineering, Data Science, and ML Engineering areas. Storyboarding & impact communication: Build effective storyboards to communicate solution impact to clients and ZS Leadership. Scaling mindset: Provide structure to client engagement, build and maintain standardized and operationalized Quality Checks on team's work, and ensure high-quality client deliverables. Team management: Export best practices and learnings to the broader team and mentor Associates on teams. What You'll Bring Bachelor's degree in Computer Science (or Statistics) from a premier institute, with strong academic performance in analytics and quantitative coursework. Knowledge of programming: Python (Deep Expertise), PySpark, SQL. Expertise in machine learning, regression, clustering, and classification models (preferably in a product environment). Knowledge of big data/advanced analytics concepts and algorithms (e.g., social listening, recommender systems, predictive modeling, etc.). Excellent oral and written communication skills. Strong attention to detail, with a value-addition mindset. Excellent critical thinking and problem-solving skills. High motivation, good work ethic, and maturity. 3-5 years of relevant post-collegiate work experience, preferably in industries like B2C or product companies, in execution roles focused on Data & Decision Sciences, Data Engineering, Stakeholder management, and building scalable processes. Should have hands-on analytics experience where the candidate has worked on algorithms/methodology from scratch and not merely executed existing codes and processes. Ability to coach and mentor juniors on the team to drive on-the-job learning & expertise building.
