Role Overview
We are looking for a hands-on Data Scientist with 2–5 years of experience in building, validating, and deploying machine learning models in real-world environments. The ideal candidate has strong expertise in time series analysis, forecasting, feature engineering, and anomaly detection, along with solid experience in regression techniques such as linear, ridge, lasso, polynomial, and logistic regression. Proficiency in Python and data libraries including Pandas, NumPy, and statsmodels is essential, with hands-on experience using ML frameworks like Scikit-learn, XGBoost, TensorFlow, or PyTorch. The role also requires strong data visualization skills using Matplotlib, Seaborn, or Plotly to communicate insights clearly. Experience deploying ML models on cloud platforms such as AWS, GCP, or Azure is highly preferred.
Responsibilities
• Forecast the Future: Design, implement, and optimize machine learning models tailored to Time Series data, including ARIMA, LSTMs, and Transformers.
• Model the Trends: Work with regression models (linear, ridge, lasso, polynomial, etc.) to analyse and predict trends, behaviours, and business outcomes.
• Data Wrangling Wizardry: Clean, preprocess, and structure Time Series and tabular data for modelling and analysis.
• Experiment and Research: Explore advanced algorithms and hybrid approaches to combine Time Series models with regression techniques.
• Optimize for Scale: Develop scalable and production-ready solutions that integrate seamlessly into real-world supply-chain systems.
• Collaborate and Innovate: Work closely with cross-functional teams, including product managers, data engineers, and other researchers, to align technical solutions with business needs.
• Stay on the Cutting Edge: Keep up with the latest advancements in machine learning, Time Series analysis, and regression modelling to keep our solutions ahead of the curve.
Requirements
• Time Series Expertise: Deep understanding of Time Series analysis, including forecasting, feature engineering, and anomaly detection.
• Regression Know-How: Strong experience building and optimizing regression models such as linear, ridge, lasso, polynomial, and logistic regression.
• ML Mastery: Hands-on experience with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Data Wizardry: Proficiency in Python, and experience working with libraries like Pandas, NumPy, and statsmodels.
• Visualization Ninja: Ability to visualize trends, forecasts, and patterns using tools like Matplotlib, Plotly, or Seaborn.
• Mathematical Rigor: Strong foundation in statistics, probability, and optimization.
• Cloud Savvy: Experience deploying ML models on platforms like AWS, GCP, or Azure.
• Bonus Points: Experience with hybrid models (e.g., combining Time Series with regression techniques), Reinforcement Learning, or real-time ML systems.
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