Lead Engineer - Data Science

Job Summary

An accomplished data science professional with 5–8 years of experience in designing, delivering, and mentoring high-impact analytical solutions. Takes full ownership of project modules, delivering high-quality results while mentoring junior engineers, guiding architectural decisions, and promoting a data-driven culture. Demonstrates strong analytical thinking, independence, and a command of tools and technologies across the AI/ML, cognitive analytics, and statistical modeling domains.

Key Responsibilities

• Perform requirement analysis, feasibility assessment, and system-level effort estimation with risk identification and mitigation.
• Architect, develop, and deploy advanced machine learning models, predictive analytics workflows, and cognitive solutions.
• Lead code reviews, design walkthroughs, and quality checks across deliverables to ensure traceability, optimization, and performance.
• Investigate root causes of analytical challenges and propose robust, technically sound solutions.
• Drive technical mentorship, skill development, and identification of training needs within the team.
• Participate in and lead internal technical initiatives; contribute to organizational capability building and knowledge sharing.
• Interface with customers and stakeholders to clarify objectives, present insights, and align deliverables with business goals.

Education & Experience

• Qualification: B.E./B.Tech, MCA, or equivalent in Computer Science, Statistics, or related disciplines
• Experience: 5–8 years in data science, AI/ML modeling, and analytics product development

Core Competencies

• Data Modeling & Insights: Exploratory analysis, pattern recognition, anomaly detection, predictive modeling
• Machine Learning & AI: Hands-on expertise with algorithms for classification, regression, clustering, and deep learning foundations
• Cognitive Analytics: Application of AI/ML for Computer Vision, NLP, recommendation systems, and statistical reasoning
• Programming Languages: R, Python, Perl, Scala
• Tools & Frameworks: RStudio, MATLAB, Spark MLlib, Python ML stack (pandas, scikit-learn, TensorFlow), SPSS, SAS
• Platform Proficiency: Unix/Linux-based development and deployment environments
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