About the job
Leverage predictive modeling techniques to enhance power generation, optimize pricing, reduce costs, and elevate customer experiences across various business outcomes.
Demonstrated expertise in statistical modeling, machine learning, probability theory, algorithms, data mining, unstructured data analytics, and natural language processing.
Proficient in advanced machine learning techniques, including Clustering, Regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modeling, dimensionality reduction, SEM, GLM, GLMM, Deep Learning, Neural Networks, Topic Modeling, Multivariate Statistics, K-NN, and Naïve Bayes.
Familiarity with widely used Deep Learning architectures, simulation strategies, scenario analysis, constraint optimization, anomaly detection, semi-supervised and unsupervised learning algorithms using deep learning.
Experience in optimization methods such as Linear Programming, Genetic Algorithms, Simulated Annealing, and Monte Carlo Simulation.
Hands-on experience with emerging technologies like deep learning, NLP, NLG, image processing, recommendation systems, chatbots, voice AI, and video AI.
Proficient in managing the end-to-end data science pipeline: from problem scoping, data discovery and extraction, exploratory data analysis (EDA), modeling, evaluation, insights generation, visualizations, to continuous improvement, maintenance, and tracking business value and impact.
Daily engagement in discussions involving various algorithms and approaches.
Oversee the complete software development lifecycle, including hands-on coding, code reviews, testing, deployment, and documentation. Familiarity with Agile SCRUM and MLOps is highly preferred.
Develop scalable and reusable assets and accelerators while implementing best practices in machine learning.
Collaborate directly with internal technical teams to ensure seamless and effective integration of solutions.
Conduct market and industry trend analysis in technology, proactively identifying opportunities to propose optimal solutions. Engage in ongoing research on emerging ML techniques and best practices.
Responsible for coding, testing, debugging, evaluating solution/technology options (including Cloud), and documenting the application development process.
Future migration of current analytics applications and pipelines to Cloud environments.
