✦Role summaryAI-generated
This role focuses on building and deploying time‑series forecasting models using statistical and deep‑learning techniques on large, multi‑source datasets. The candidate will also clean, transform, and analyze data while leveraging tools such as Python, PySpark, and KubeFlow to deliver actionable insights.
About this role
Data Scientist
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
Specialization
Data Science Advanced: Data Specialist
Job requirements
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 and clean, process, and transform data for modeling purposes. 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. Key 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. 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). 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. Similar open roles
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