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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Science & AI Specialist - **Company:** ABB Ltd - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Cleansing, Data Visualization, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Regression Analysis, Power BI, Tensorflow, SQL Databases, Data Processing, Feature Engineering, Pytorch, Random Forest, Model Validation, Matplotlib, Scikit Learn, Information Technology, Xgboost, Performance Monitor, Plotly, Machine Learning Operations, Streamlit Framework, Data Pipelines, Recurrent Neural Networks - **Published:** September 21, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/plrf6s6b6c ## About the Role * Degree in B.Tech / M.Tech / BCA / MCA (Computer Science, Data Science, Statistics, or related field). * 5-7 years of hands-on experience in driving forecasting and predictive analytics solutions using AI/ML techniques in a business or finance environment * Strong, demonstrated expertise in traditional time series methods (ARIMA, SARIMA, Exponential Smoothing, etc.) and classical ML algorithms (regression models, XGBoost, Random Forest, LSTM). * Proven experience managing the complete ML lifecycle - from data pipeline design and feature engineering through model training, validation, deployment, and performance monitoring. Hands-on experience with feature importance/driver analysis and model explain ability techniques (SHAP analysis or equivalent). * Advanced proficiency in Python and SQL for data manipulation, modeling, and statistical analysis. Strong experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or equivalent. * Strong statistical knowledge including hypothesis testing, regression analysis, and model validation techniques. Experience with data visualization tools (Power BI, Streamlit, Python libraries like Plotly, Matplotlib). ## Description * Design, develop, and operationalize ML-driven models to forecast revenue, costs, cash flow, and other key financial metrics - owning the complete ML lifecycle from data preparation and feature engineering through deployment and monitoring * Explore and implement forecasting and scenario analysis approaches (classical ML, time series methods, statistical techniques, etc) to optimize accuracy, explainability, and operational feasibility * Product Owner for Forecasting: define and own the product vision, roadmap, and backlog for forecasting data products, ensuring alignment with business strategy and stakeholder needs * Perform feature importance and driver analysis to identify key factors influencing financial outcomes as well as apply model explainability techniques to build stakeholder trust in predictions * Translate model outputs into actionable insights for Finance, Sales, and other Management Functions * Develop dashboards and automated reports to track accuracy and financial performance over time * Conduct root-cause analysis on variances between predicted and actual outcomes, and recommend corrective actions * Facilitate cross-functional engagement with business stakeholders to refine forecasting assumptions and validate model outputs ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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