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
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Perform exploratory data analysis and feature engineering across multiple data sources
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Design, train, validate, and
backtest
supervised ML models for classification and regression tasks
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Build time-series and probabilistic forecasting models for demand and inventory signals
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Develop survival analysis models to predict time-to-event outcomes
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Build explainability pipelines (SHAP) to translate model outputs into business-readable root cause attribution
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Conduct causal analysis and process mining to identify workflow patterns driving business outcomes
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Validate model outputs with business SMEs and refine based on feedback
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Define and agree model performance thresholds, feature importance priorities, and acceptance criteria with client stakeholders
Required Skills
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3–5 years of experience in data science, machine learning, or advanced analytics
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Strong experience in supervised ML (
XGBoost
,
LightGBM
, Random Forest, scikit-learn)
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Experience with time-series forecasting (Prophet, ARIMA) and survival analysis
-
Proficiency in SHAP and other model explainability frameworks