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5 days ago
Requirements and responsibilities
We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.Key ResponsibilitiesDesign, develop, and deploy Machine Learning models for business-critical use cases.Build and optimize traditional ML models such as: Linear Regression; Logistic Regression; Decision Trees; Random Forest; Gradient Boosting (XGBoost, LightGBM, CatBoost); Support Vector Machines; Clustering Algorithms.Develop forecasting solutions using: ARIMA / SARIMA; Prophet; Exponential Smoothing; Time-Series Regression Models.Perform exploratory data analysis (EDA), feature engineering, and data validation.Evaluate model performance using appropriate statistical and business metrics.Work with structured and semi-structured datasets from multiple sources.Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.Build scalable data pipelines and support model deployment in production environments.Monitor model performance, identify data drift, and implement model retraining strategies.Present insights and recommendations to technical and non-technical stakeholders.5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.Technical SkillsMachine LearningStrong understanding of supervised and unsupervised learning algorithms.Experience with ensemble methods and advanced ML techniques.Expertise in model selection, hyperparameter tuning, and performance optimization.Forecasting & StatisticsStrong understanding of: Time-Series Analysis; Forecasting Techniques; Statistical Inference; Hypothesis Testing; Probability Distributions; A/B Testing.ProgrammingAdvanced proficiency in Python.Experience with: Pandas; NumPy; Scikit-learn; Statsmodels; XGBoost / LightGBM; Prophet.Data & SQLStrong SQL skills with experience in complex queries and performance optimization.Experience working with large-scale datasets.VisualizationExperience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.Cloud & MLOps (Preferred)Exposure to AWS, Azure, or GCP.Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.