AI/ML Computational Science Specialist

Role Overview: You will be joining Accenture as an AI/ML Computational Science Specialist. As part of the Accenture Operations team, you will be a key player in executing advanced analytics projects from design to execution. Your role will involve collaborating with data scientists, subject matter experts, sales, and delivery teams from various Accenture locations globally to deliver strategic solutions. Key Responsibilities: - Analyze and solve moderately complex problems, potentially requiring the creation of new solutions or adaptation of existing methods. - Understand the strategic direction set by senior management and align it with team goals. - Collaborate with peers, management levels within Accenture, and clients. - Receive guidance on determining methods and procedures for new assignments. - Make decisions that directly impact your team. - Manage small teams or work independently on projects, either at client sites or within Accenture. - Be prepared to work in rotational shifts to ensure flexibility and adaptability to meet team and client needs. Qualifications Required: - Masters/Ph.D. in Statistics, Mathematics, Computer science, Economics, Physics, Engineering, or related scientific disciplines / MBA in Business Analytics / 4 years undergraduate from any Tier1 Institute - Minimum 5 to 8 years of industry experience in Financial Services, Capital Markets, Healthcare, Pharmaceuticals, and Retail, with hands-on expertise in data-driven decision-making - Proficiency in statistical tools and languages such as Python, R, and Alteryx - Proficiency in Multivariate Statistics like Regression Analysis, Logistic Analysis, PCA, Factor Analysis, Cluster Analysis, Generalized Linear Mixed Models, Linear Mixed Effects Model - Knowledge of Supervised and Unsupervised learning techniques like Random Forest, Gradient Boosting Machines, Decision Tree, SVM, KNN, k-Means Clustering, DBSCAN - Familiarity with Forecasting techniques including ARIMA/SARIMA/ARIMAX, LSTM, GRU, Prophet, Ensemble Methods, Bayesian Structural Time Series - Experience with Boosting Algorithms like Gradient Boosting Machines (GBM), XGBoost, LightGBM, Adaboost, CatBoost - Understanding of visualization tools and packages such as Tableau, PowerBI, Google Data Studio, Qlik, R-Shiny, Django, Streamlit, DASH, tkinter - Knowledge of relational databases, NLP/Text Analytics, Deep learning techniques, GenAI, Simulation methods, Optimization methods, Machine Learning tools, Cloud-based computing platforms, and Excel analysis tools (Note: The additional details about Accenture have been omitted as they were not present in the job description) Role Overview: You will be joining Accenture as an AI/ML Computational Science Specialist. As part of the Accenture Operations team, you will be a key player in executing advanced analytics projects from design to execution. Your role will involve collaborating with data scientists, subject matter experts, sales, and delivery teams from various Accenture locations globally to deliver strategic solutions. Key Responsibilities: - Analyze and solve moderately complex problems, potentially requiring the creation of new solutions or adaptation of existing methods. - Understand the strategic direction set by senior management and align it with team goals. - Collaborate with peers, management levels within Accenture, and clients. - Receive guidance on determining methods and procedures for new assignments. - Make decisions that directly impact your team. - Manage small teams or work independently on projects, either at client sites or within Accenture. - Be prepared to work in rotational shifts to ensure flexibility and adaptability to meet team and client needs. Qualifications Required: - Masters/Ph.D. in Statistics, Mathematics, Computer science, Economics, Physics, Engineering, or related scientific disciplines / MBA in Business Analytics / 4 years undergraduate from any Tier1 Institute - Minimum 5 to 8 years of industry experience in Financial Services, Capital Markets, Healthcare, Pharmaceuticals, and Retail, with hands-on expertise in data-driven decision-making - Proficiency in statistical tools and languages such as Python, R, and Alteryx - Proficiency in Multivariate Statistics like Regression Analysis, Logistic Analysis, PCA, Factor Analysis, Cluster Analysis, Generalized Linear Mixed Models, Linear Mixed Effects Model - Knowledge of Supervised and Unsupervised learning techniques like Random Forest, Gradient Boosting Machines, Decision Tree, SVM, KNN, k-Means Clustering, DBSCAN - Familiarity with Forecasting techniques including ARIMA/SARIMA/ARIMAX, LSTM, GRU, Prophet, Ensemble Methods, Bayesian Structural Time Series - Experience with Boosting Algorithms like Gradient Boosting Machines (GBM), XGBoost, LightGBM, Adaboost, CatBoost - Understanding of visualization tools and packages such as Tableau, PowerBI, Google Data Studio, Qlik, R-Shiny, Django, Streamlit, DASH, tkinter - Knowledge of relational data