Key Responsibilities:
Develop & Deploy machine learning models for predictive analytics and automation. Big Data Processing – Work with large-scale datasets to uncover trends and insights. Feature Engineering – Design and optimize features to improve model accuracy. AI & NLP Integration – Implement cutting-edge AI techniques for real-world applications. Collaborate with Cross-Functional Teams – Work with engineers, product managers, and business analysts. Experimentation & A/B Testing – Conduct experiments to measure the impact of data-driven solutions. Optimize Data Pipelines – Enhance ETL workflows for efficient data processing. Visualization & Reporting – Present complex findings in an easy-to-understand manner.
Must-Have Skills:
Programming Expertise – Proficiency in Python, R, or Scala. Machine Learning & AI – Strong knowledge of supervised/unsupervised learning, deep learning, and reinforcement learning. Big Data Technologies – Experience with Spark, Hadoop, or similar frameworks. Data Engineering – Hands-on experience with SQL, NoSQL, and data warehousing. Cloud Platforms – Familiarity with AWS, GCP, or Azure for scalable data solutions. Statistical & Mathematical Skills – Proficiency in probability, statistics, and optimization techniques. Visualization Tools – Experience with Tableau, Power BI, or Matplotlib.
Nice-to-Have Skills:
Experience with Natural Language Processing (NLP) and Computer Vision. Knowledge of MLOps frameworks for model deployment. Hands-on experience with AutoML & model interpretability techniques.
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