About the Role

We are seeking a highly analytical Data Scientist with a strong background in mathematics, statistics, and machine learning to develop advanced forecasting and predictive models. This role focuses on rigorous quantitative analysis, time series modeling, statistical inference, and research-driven experimentation. The successful candidate will work with large-scale temporal datasets and collaborate with engineers and domain experts to build production-ready forecasting solutions.

Key Responsibilities

• Develop statistical and machine learning models for forecasting, prediction, and anomaly detection.
• Build robust time series forecasting models using classical statistical and modern deep learning approaches.
• Perform exploratory data analysis to identify trends, seasonality, structural breaks, and anomalies.
• Engineer features from temporal, multivariate, and high-dimensional datasets.
• Design model evaluation frameworks using appropriate forecasting metrics and backtesting methodologies.
• Conduct hypothesis testing, uncertainty quantification, and probabilistic analysis.
• Optimize models through hyperparameter tuning and cross-validation.
• Research and implement state-of-the-art forecasting techniques from academic literature.
• Collaborate with software engineers to deploy and monitor models in production.
• Document experiments, methodologies, assumptions, and model performance.

Required Qualifications

• Bachelor’s or Master’s, Data Science, Mathematics, Statistics, Operations Research, Physics, Economics, or a related quantitative discipline.
• Strong foundation in probability, statistics, linear algebra, calculus, and optimization.
• Excellent programming skills in Python and SQL.
• Hands-on experience working with large-scale datasets and scientific computing libraries.

Technical Skills

Time Series & Statistical Modeling

• ARIMA, SARIMA, ETS, Prophet
• State Space Models and Kalman Filters
• VAR, Cointegration, Bayesian Time Series
• Forecast evaluation, backtesting, uncertainty estimation

Machine Learning

• Scikit-learn, XGBoost, LightGBM, CatBoost
• Random Forest, Support Vector Machines, Ensemble Learning
• Feature Engineering and Dimensionality Reduction

Deep Learning

• LSTM, GRU, Temporal Convolutional Networks (TCN)
• Transformer-based forecasting models (TFT, PatchTST, N-BEATS)
• PyTorch or TensorFlow

Preferred Qualifications

• Experience with weather, energy, finance, or industrial forecasting.
• Knowledge of geospatial or remote sensing datasets is a plus.
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps.
• Research publications or open-source contributions are advantageous.

What We’re Looking For

• Strong mathematical thinking and problem-solving ability.
• Ability to independently read and implement research papers.
• Excellent communication and collaboration skills.
• Curiosity, ownership, and a passion for solving challenging quantitative problems.