About the Role:

We are seeking an experienced Data Scientist with strong expertise in Classic Machine Learning techniques to join our team. The ideal candidate will have a proven track record of designing, building, and deploying predictive/statistical models, with a deep understanding of algorithms such as regression, tree-based methods, boosting, clustering, and time-series forecasting. This role requires someone who can combine statistical rigor, machine learning expertise, and business acumen to generate actionable insights and solve complex problems.

Key Responsibilities:

Data Exploration & Analysis:

- Gather, clean, and analyze large datasets from multiple sources.

- Perform statistical analysis and hypothesis testing to identify trends, patterns, and relationships.

Model Development & Implementation:

- Build, validate, and deploy models using Classic ML techniques:

Regression: Linear, Logistic:

- Tree-based & Ensemble Models: Random Forest, Gradient Boosting, XGBoost, LightGBM

- Clustering & Unsupervised Learning: K-Means, Hierarchical Clustering

- Statistical & Predictive Modelling for business use cases

- Develop Time-Series Forecasting Models (ARIMA, SARIMA, Prophet, ETS, etc.) for demand, sales, or trend prediction.

Performance Optimization:

- Conduct feature engineering, model tuning, and hyperparameter optimization.

- Evaluate models using statistical metrics (AUC, RMSE, MAE, R- , Precision/Recall, etc.).

Business Problem Solving:

- Translate business problems into analytical frameworks.

- Provide data-driven recommendations to stakeholders for strategic and operational decisions.

Collaboration & Deployment:

- Work with data engineers to ensure scalable data pipelines.

- Collaborate with cross-functional teams (Product, Marketing, Operations, Engineering).

- Deploy models into production and monitor performance.

Required Skills & Experience:

- Education: Master's or Bachelor's in Computer Science, Statistics, Mathematics, Data Science, or related field.

- Experience: 7+ years in Data Science with a strong focus on Classic Machine Learning and Statistical Modelling.

Technical Expertise:

- Hands-on experience with algorithms: Logistic Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, K-Means, ARIMA/SARIMA.

- Strong background in statistical analysis, hypothesis testing, and predictive modelling.

- Experience in time-series forecasting and trend analysis.

Programming & Tools:

- Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, PyCaret, etc.) and/or R. SQL for data querying.

- Familiarity with big data platforms (Spark, Hadoop) is a plus.

- Exposure to cloud ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI) preferred.

Other Skills:

- Strong problem-solving, critical thinking, and communication skills.

- Ability to explain complex models and statistical results to non-technical stakeholders.

Good to Have:

- Exposure to deep learning concepts (not mandatory, but an added advantage).

- Experience in MLOps practices - CI/CD for ML, model monitoring, model drift detection.

- Knowledge of domain-specific applications (finance, healthcare, retail, supply chain).