DE&A - AIML - Data Science - Machine Learning

Data Scientist

Role Overview Responsible for designing, building, and validating predictive and explanatory models that generate actionable business insights. Works closely with business stakeholders to ensure model outputs are accurate, interpretable, and aligned to real-world decision-making.

Experience Required: 3–5 years

Key Responsibilities

• Perform exploratory data analysis and feature engineering across multiple data sources
• Design, train, validate, and backtest supervised ML models for classification and regression tasks
• Build time-series and probabilistic forecasting models for demand and inventory signals
• Develop survival analysis models to predict time-to-event outcomes
• Build explainability pipelines (SHAP) to translate model outputs into business-readable root cause attribution
• Conduct causal analysis and process mining to identify workflow patterns driving business outcomes
• Validate model outputs with business SMEs and refine based on feedback
• Define and agree model performance thresholds, feature importance priorities, and acceptance criteria with client stakeholders

Required Skills

• 3–5 years of experience in data science, machine learning, or advanced analytics
• Strong experience in supervised ML (XGBoost, LightGBM, Random Forest, scikit-learn)
• Experience with time-series forecasting (Prophet, ARIMA) and survival analysis
• Proficiency in SHAP and other model explainability frameworks
• Strong Python skills (pandas, numpy, scikit-learn)
• Ability to communicate complex model outputs clearly to non-technical business stakeholders
• Experience in supply chain, retail, or inventory analytics preferred

Data Scientist

Role Overview Responsible for designing, building, and validating predictive and explanatory models that generate actionable business insights. Works closely with business stakeholders to ensure model outputs are accurate, interpretable, and aligned to real-world decision-making.

Experience Required: 3–5 years

Key Responsibilities

• Perform exploratory data analysis and feature engineering across multiple data sources
• Design, train, validate, and backtest supervised ML models for classification and regression tasks
• Build time-series and probabilistic forecasting models for demand and inventory signals
• Develop survival analysis models to predict time-to-event outcomes
• Build explainability pipelines (SHAP) to translate model outputs into business-readable root cause attribution
• Conduct causal analysis and process mining to identify workflow patterns driving business outcomes
• Validate model outputs with business SMEs and refine based on feedback
• Define and agree model performance thresholds, feature importance priorities, and acceptance criteria with client stakeholders

Required Skills

• 3–5 years of experience in data science, machine learning, or advanced analytics
• Strong experience in supervised ML (XGBoost, LightGBM, Random Forest, scikit-learn)
• Experience with time-series forecasting (Prophet, ARIMA) and survival analysis
• Proficiency in SHAP and other model explainability frameworks
• Strong Python skills (pandas, numpy, scikit-learn)
• Ability to communicate complex model outputs clearly to non-technical business stakeholders
• Experience in supply chain, retail, or inventory analytics preferred

Data Scientist

Role Overview Responsible for designing, building, and validating predictive and explanatory models that generate actionable business insights. Works closely with business stakeholders to ensure model outputs are accurate, interpretable, and aligned to real-world decision-making.

Experience Required: 3–5 years

Key Responsibilities

• Perform exploratory data analysis and feature engineering across multiple data sources
• Design, train, validate, and backtest supervised ML models for classification and regression tasks
• Build time-series and probabilistic forecasting models for demand and inventory signals
• Develop survival analysis models to predict time-to-event outcomes
• Build explainability pipelines (SHAP) to translate model outputs into business-readable root cause attribution
• Conduct causal analysis and process mining to identify workflow patterns driving business outcomes
• Validate model outputs with business SMEs and refine based on feedback
• Define and agree model performance thresholds, feature importance priorities, and acceptance criteria with client stakeholders

Required Skills

• 3–5 years of experience in data science, machine learning, or advanced analytics
• Strong experience in supervised ML (XGBoost, LightGBM, Random Forest, scikit-learn)
• Experience with time-series forecasting (Prophet, ARIMA) and survival analysis
• Proficiency in SHAP and other model explainability frameworks
• Strong Python skills (pandas, numpy, scikit-learn)
• Ability to communicate complex model outputs clearly to non-technical business stakeholders
• Experience in supply chain, retail, or inventory analytics preferred