rohit wayal

Open to

Bangalore · Mumbai · Delhi

Work style

Full-time, Remote, Hybrid, Onsite

Availability

Available in 30 days

Contact details — on requestProof of Work — on request

Experience

DATA SCIENTIST / TECHNICAL SUPPORT ENGINEER

DG FUTURETECH INDIA PVT LTD · Oct 2024 – Present

Not yet confirmed

Built churn prediction model improving customer retention revenue by 12%.
Engineered behavioral and transactional features.
Applied probability-based risk segmentation for insurance company.
Enabled targeted retention campaign strategies.

DATA SCIENTIST / ASSOCIATE TECHNICAL SUPPORT ENGINEER

IDEAS REVENUE SOLUTIONS (SHREE SWAMI SAMARTH INDUSTRIAL SERVICES PRIVATE LIMITED) · Jun 2023 – Oct 2024

Not yet confirmed

Developed time-slot level staffing demand prediction using XGBoost regression.
Applied weekday/weekend segmentation and seasonality decomposition.
Reduced overstaffing costs while maintaining service levels.
Delivered visualization dashboards for operational decision-making.
Built end-to-end attrition prediction model using XGBoost & Scikit-learn, enabling proactive workforce retention strategy.
Engineered tenure, hierarchy, and business segmentation features; handled class imbalance using SMOTE.
Applied stratified cross-validation and probability calibration for accurate risk scoring.
Delivered employee-level attrition risk dashboards supporting HR intervention planning
Built an XGBoost Multi-Class Classification model to predict enterprise system outage severity using operational telemetry and event log data.
Performed EDA, feature engineering, data preprocessing, and hyperparameter tuning using Stratified Cross-Validation.
Engineered lag, rolling, and temporal features to improve prediction accuracy and model performance.
Evaluated the model using Fl-Score, ROC-AUC, Confusion Matrix, and Log Loss, enabling proactive outage detection and reduced downtime.
Built an XGBoost-based forecasting model to predict SKU-level sales and revenue, incorporating pricing, promotional, seasonal, and lag-driven demand features.
Modeled price sensitivity and promotional uplift to quantify revenue and margin impact under different pricing scenarios.
Identified key revenue drivers using feature importance analysis, enabling data-driven pricing, inventory optimization, and margin improvement strategies.
Built ML models to predict loan default using Machine Learning classification model.
Evaluated performance using ROC-AUC, Fl-score, Precision-Recall.
Implemented hyperparameter tuning and regularization techniques.
Designed hybrid forecasting pipeline using LSTM + XGBoost for revenue and unit sales prediction.
Engineered lag, rolling, and macroeconomic features for time series modeling.
Implemented rolling backtesting with MAE, RMSE, MAPE evaluation.
Built Streamlit dashboard for scenario-based revenue simulation

Skills 0 proven through work

Also works with

Monitoring AutomationRecall Metric InterpretationTaking OwnershipCrisis ManagementTechnical TroubleshootingClient OnboardingSoftware ConsultingClean Code PracticesDatabase Query OptimizationPostgreSQL Database UsageDashboard UI DesignLogistic Regression ModelingData ValidationData ModelingTime Series Trend AnalysisNoisy Data HandlingRandom Forest ModelingModel Selection Trade-Off AnalysisTabular Data Cleaning & ValidationModel-Based ImputationPython DevelopmentETL Pipeline DevelopmentData EngineeringWorkforce PlanningFault-Tolerant System DesignAccuracy Metric InterpretationModel Performance ImprovementFeature SelectionK-Fold Cross-ValidationGridSearchCV UsageHyperparameter TuningTemporal Order PreservationAI Bias MitigationSampling MethodsTrain-Test Split ValidationUptime ManagementClassification ModelingFailure Prediction ModelingCustomer Retention and Churn PreventionStakeholder Communication AdaptationExplaining Technical ConceptsMachine Learning Model DeploymentPickle SerializationModel Error AnalysisF1 Score InterpretationExplainable AIOverfitting Detection and MitigationXGBoost ModelingSMOTE UsageHandling Data ImbalanceTeamworkMaster Data ManagementData Unification and NormalizationCustomer Churn PredictionBusiness AnalysisProject ExecutionModel Metric Selection & InterpretationMachine Learning ApplicationFeature EngineeringExploratory Data Analysis (EDA)Data PreprocessingData CollectionProblem SolvingRequirements AnalysisArtificial Intelligence FundamentalsData Science

Proof of Work

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Education

Master of Science, Computer Applications

SAVITRIBAI PHULE PUNE UNIVERSITY

Bachelor, Computer Applications - Science

SAVITRIBAI PHULE PUNE UNIVERSITY

Contact details

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