Data Engineer

Responsibilities:Analyze large time-series data sets to identify patterns, trends, and anomalies.Build, test/validate ML/Deep learning models for time-series data analytics.Collaborate with cross-functional teams to understand business requirements and provide data-driven solutions.Development and Debugging code.Apply statistical analysis and hypothesis testing techniques to validate/optimize models.Implement iterative development processes to refine/improve data analytics models.Stay updated with the latest advancements in time-series data analytics, machine learning, and deep learning techniques.Experience:3 - 5 years of proven experience - in building machine learning and deep learningStrong proficiency in Python and its data science libraries like NumPy, Pandas, Statsmodels, and TensorFlow models for time-series data analytics.Familiarity with time-series analysis techniques, including PyCaret, GluonTS, TensorFlow, Probability, ARIMA, LSTM, and Prophet.R-Shiny/ Streamlit development/deployment exposure - Basic levelHands on Experience R/PythonExperience with data preprocessing, feature engineering, and model evaluation for time-series data.Proficiency in linear/non-linear modeling, statistical analysis, and hypothesis testing.Strong problem-solving skills and the ability to work independently or as part of a team.Excellent communication and presentation skills to convey complex concepts to both technical and non-technical stakeholders.