Data Analyst

Required Qualifications

10+ years of being a practitioner in data engineering or a related field.

Bachelor's or master's degree in computer science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.

Strong programming skills in languages such as Python or R; familiarity with SQL and distributed computing frameworks (e.g., Spark, Hadoop) is a plus.

Proficiency in data visualization tools such as Matplotlib, PowerBI, Tableau.

Solid understanding of probability, statistics, hypothesis testing, and data modeling concepts.

Experience with cloud platforms (e.g., AWS, GCP, Azure) for data storage, processing, and model deployment is beneficial.

Excellent communication and collaboration skills, with the ability to explain complex technical concepts to diverse audiences.

Strong attention to detail, analytical thinking, and problem-solving abilities.

Preferred Qualifications

Experience working on large-scale data science projects or in industry domains such as finance, healthcare, retail, or technology.

Familiarity with MLOps practices, model versioning, and monitoring tools.

Knowledge of natural language processing (NLP), computer vision, or time-series analysis.

Contributions to open-source projects or publications in relevant conferences/journals.

Develop and maintain data pipelines and ETL processes to ensure reliable and scalable data flow where applicable.

Develop and implement machine learning algorithms and statistical models specifically aimed at analyzing technology metrics, such as system performance, resource utilization, adherence to technology principles etc there by helping achieve improved resource utilization, boosting system reliability, reducing technical debt etc .

Hands-on experience with machine learning libraries and frameworks, such as scikit-learn, TensorFlow, Keras, PyTorch, or XGBoost.