Data Scientist

Key Responsibilities

• Build, validate, and maintain machine learning models for forecasting, classification, regression, anomaly detection, and business analytics.
• Perform data cleaning, feature engineering, model evaluation, and performance monitoring to ensure reliable and scalable solutions.
• Develop data pipelines, automation workflows, and analytical solutions using Python and SQL.
• Analyze business data to identify trends, risks, opportunities, and key performance drivers, translating findings into actionable recommendations.
• Collaborate with business, operations, finance, and technology teams to understand requirements and deliver measurable analytical outcomes.
• Communicate model assumptions, limitations, and results effectively to both technical and non-technical stakeholders.
• Own data science projects end-to-end, including requirement gathering, development, deployment, and stakeholder management.
• Mentor junior team members and contribute to improving data science best practices across the organization.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, Finance, or a related quantitative discipline.
• 4+ years of experience in Data Science, Machine Learning, Predictive Analytics, or Statistical Modeling.
• Strong programming skills in Python and SQL with hands-on experience building production-ready analytical workflows.
• Solid understanding of regression, classification, clustering, forecasting, anomaly detection, feature engineering, and model evaluation techniques.
• Experience with machine learning libraries such as Scikit-learn, XGBoost, LightGBM, Statsmodels, TensorFlow, or PyTorch.
• Strong communication, stakeholder management, problem-solving, and project ownership skills.

Preferred Qualifications

• Experience in business, financial, or operations analytics, consulting, or client-facing analytical environments.
• Exposure to model deployment, monitoring, and MLOps practices.
• Familiarity with Flask, FastAPI, Docker, Git, CI/CD pipelines, Power BI, Tableau, Streamlit, Plotly, and cloud platforms such as AWS, Azure, or GCP.
• Experience leading analytical projects and mentoring junior team members.