Data Scientist

Employment Type: Employee

Position Overview

This role focuses on developing and implementing advanced time series forecasting models to predict future trends, demand, and performance metrics. The Data Scientist will leverage statistical methods, machine learning, and deep learning techniques, working with large datasets to drive business decisions.

Key Responsibilities

• Develop and implement time series forecasting models to predict future trends, demand, and performance metrics.
• Leverage statistical methods, machine learning, and deep learning techniques for accurate forecasting.
• Work with large datasets from multiple sources, cleaning, processing, and transforming data for modeling.
• Analyze historical data patterns and trends to identify key drivers of future outcomes.
• Perform data validation, anomaly detection, and ensure data integrity.
• Collaborate with business and technical teams to understand forecasting needs and ensure models align with business objectives.
• Communicate findings, insights, and predictions to both technical and non-technical stakeholders.
• Continuously optimize and improve forecasting models by experimenting with new algorithms, tools, and techniques.
• Use forecasting models to drive business decisions related to supply chain, demand planning, inventory, and more.
• Document processes, models, and code to ensure scalability and reproducibility.

Requirements

• Education: Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field.
• Experience: 5-6 years of experience in data science with a focus on time series forecasting.
• Technical Skills:
  • Primary Skills: Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools (KubeFlow, BentoML), T-Test, Z-Test.
  • Proficiency in Python or R for data analysis and modeling.
  • Strong knowledge of time series forecasting techniques such as ARIMA, SARIMA, ETS, Prophet, or LSTM.
  • Experience with machine learning algorithms and statistical analysis.
  • Familiarity with tools like SQL, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
  • Experience working with large datasets and data pipelines in a cloud-based environment (AWS, Azure, or GCP).
• Soft Skills:
  • Strong problem-solving skills and the ability to interpret complex data.
  • Excellent communication and presentation skills to translate complex data findings into actionable insights for business teams.

Specialization

• Data Science Advanced: Data Specialist

Company Information

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Salary

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Location Type

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