p>About the Ideal Candidate: You'll have:
• A strong understanding of Data Science, including basic elements of machine learning, statistics, probability, and modeling • Strong experience with time series analysis and forecasting techniques (ARIMA, exponential smoothing, Prophet, LSTM, etc.) • A quantitative background with experience working with time series data and strong coding skills • Background in deep learning and neural network architectures for sequence modeling • Experience with Data Science programming languages: Python (required), R, Matlab • Familiarity with Deep Learning frameworks such as TensorFlow and PyTorch, with experience in at least one • Applied knowledge of ML techniques/algorithms including linear models, neural networks, decision trees, Bayesian techniques, clustering, and anomaly detection • 2+ years of experience in building machine learning models • Experience with cloud platforms (AWS, GCP, or Azure) • Strong written and verbal communications, ability to translate complex technical topics to stakeholders • Ability to form strong working relationships with team members, customers' technical teams, and executive leadership • Degree in Computer Science, Statistics, Physics, Mathematics, Engineering, or a related field.
You Will:
• Build forecasting models for demand planning, power/price prediction, and supply chain optimization • Develop time series models using traditional methods (ARIMA, Prophet) and modern ML approaches (LSTM, Transformers) • Partner with project teams in developing and applying ML expertise to deliver customer solutions • Independently and effectively engage with external technical stakeholders and subject matter experts to understand and solve critical business problems through artificial intelligence • Design and deploy machine learning models for commercial and industrial applications, including anomaly detection, prescriptive maintenance, and optimization • Lead all phases of the data science process from data exploration, feature engineering, model training, testing, and deployment • Apply data mining techniques, statistical analysis, and build prediction systems • Create automated anomaly detection systems and track performance • Communicate complex technical topics to internal and external stakeholders.