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Tier-1 employer, popular mid-level data role, metro location, and broad AI/data skill requirements amplify competition.
Data engineering skills are broadly transferable across industries despite some pharma-specific knowledge.
Explicit 3–6 years, specific data/AI skills, and regulated pharma environment raise screening rigidity.
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Design, build, and optimize scalable data architectures specifically for AI/ML consumption and business intelligence.
Collaborate with business analysts, solution architects, and developers to transform raw data into strategic assets driving operational excellence and business efficiencies.
Provide technical mentorship, promote knowledge sharing, and align development with global strategies, including involvement in organizational events and initiatives beyond standard projects.
Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Software Engineering, Mathematics, or Statistics.
3-6 years of experience in data science and engineering with proven success in driving small to medium initiatives.
Proficiency in advanced SQL, data manipulation, and AI/ML concepts including feature stores, vector embeddings, or training data pipelines.
Demonstrated experience in technical mentoring, knowledge sharing events, and good understanding of cybersecurity and secure software development practices.
Experienced in architecting data solutions optimized for AI/ML and business intelligence use cases within corporate or R&D environments.
Comfortable managing stakeholder relationships and aligning technical solutions with overarching organizational strategies.
Proactive in addressing technical debt, performance bottlenecks, and architectural challenges with a strategic, innovation-driven approach.