Job Description & How to Apply Below
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, Stats models, Tensor Flow, 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.
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