Entry-Level Engineer – Python, Data Analytics & Generative AI Life Sciences Commercial

Cognizant is a leading technology and engineering company working with top global pharmaceutical companies. They are seeking a motivated Entry-Level Engineer with a strong foundation in Python, data analytics, and Generative AI to analyze data and contribute to AI-driven solutions in the Life Sciences sector.

Responsibilities:

• Develop and maintain Python-based scripts and applications for data analysis and modeling
• Collect, clean, and analyze structured and unstructured data from multiple sources
• Build and evaluate statistical, predictive, or machine learning models
• Assist in the development and experimentation of Generative AI solutions (e.g., LLM-based applications, prompt engineering, embeddings)
• Create data visualizations and dashboards to communicate insights effectively
• Collaborate with senior engineers, data scientists, and business stakeholders
• Document code, models, and analytical findings clearly and thoroughly
• Stay current with emerging trends in data analytics, AI, and Generative AI technologies
• Hands-on experience working with real-world data and AI solutions
• Mentorship from experienced engineers and data scientists
• Opportunities to grow skills in Generative AI and advanced analytics
• Collaborative and learning-focused work environment

Requirements:

• Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, or a related field
• Strong proficiency in Python (e.g., NumPy, Pandas, Matplotlib/Seaborn)
• Understanding of data analytics concepts, including data cleaning, exploratory analysis, and basic statistics
• Exposure to modeling techniques such as regression, classification, or time-series analysis
• Familiarity with Generative AI concepts, such as: Large Language Models (LLMs), Prompt engineering, APIs for GenAI platforms (e.g., OpenAI, Azure OpenAI, Hugging Face)
• Basic knowledge of SQL and working with databases
• Strong problem-solving, analytical, and communication skills
• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch)
• Hands-on experience through internships, academic projects, or personal projects
• Familiarity with cloud platforms (Azure, AWS, or GCP)
• Understanding of MLOps or model deployment concepts
• Experience using Git or other version control systems
• Exposure to data visualization tools (Power BI, Tableau, or similar)
• Life Sciences Commercial experience / understanding of modeling techniques