Applied Data Scientist

Position: Applied Data Scientist
Experience: 3–5 Years
Relevant Experience: 3+ Years
Key Responsibilities
Build, validate, deploy, and monitor machine learning models that support business decisions.
Work extensively with Python and SQL for data extraction, analysis, modelling, and automation.
Perform statistical analysis, exploratory analysis, root-cause analysis, segmentation, forecasting, and trend analysis.
Develop supervised and unsupervised ML models including classification, propensity modelling, clustering, segmentation, and anomaly detection.
Perform feature engineering and build reusable analytical/feature pipelines.
Evaluate models using appropriate metrics such as precision, recall, calibration, and performance on imbalanced datasets.
Monitor deployed models for data, feature, and model performance drift and support retraining/retuning.
Create analytical datasets, business KPIs, data-quality checks, dashboards, and visualizations.
Translate ambiguous business requirements into analytical problems, hypotheses, and measurable outcomes.
Collaborate with business, product, data engineering, technology teams, and customers.
Clearly communicate analytical findings, model methodology, limitations, and recommendations to technical and non-technical stakeholders.
Mandatory Skill
3+ years of hands-on Applied Data Science / Decision Science
Production ML model development and deployment
Strong Python and SQL
Machine Learning and Statistics
Feature Engineering
Scikit-learn
XGBoost / LightGBM
Model Evaluation and Validation
Model Monitoring / Drift Detection
Experience working directly with customers/stakeholders and customer data
Ability to explain ML models and analytical findings to non-technical audiences

Work arrangement
No