• Professional Experience: ~5 years of hands-on experience in data science roles with demonstrated impact in business applications.
• Technical Expertise:
o Strong programming skills in Python or R; proficiency with SQL.
o Experience with machine learning libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
o Solid understanding of data structures, algorithms, and software engineering practices.
• Mathematics & Statistics:
o Strong foundation in probability, linear algebra, calculus, and optimization.
o Applied experience in hypothesis testing, regression, clustering, classification, and time-series analysis.
• Research & Application: Proven ability to apply academic research techniques and advanced algorithms to solve practical business problems.
• Data Handling: Expertise in working with structured and unstructured data, large-scale datasets, and cloud-based platforms (Azure, AWS, or GCP).
• Communication: Ability to simplify complex findings and present them to diverse stakeholders.
Skills
• Experience in deploying ML models into production systems.
• Familiarity with big data technologies (e.g., Spark, Hadoop).
• Knowledge of MLOps practices for model lifecycle management.
• Exposure to deep learning, NLP, or recommendation systems.
• Background in applied economics, operations research, or optimization problems is a plus.