Data Scientist – Machine Learning Development & Deployment

The ideal candidate is passionate about data, machine learning, scale, and delivering production-ready AI solutions. You will use your strong analytical mindset and collaboration skills to solve complex business problems, build scalable ML systems, and drive data-driven decision making.

Responsibilities

• Analyze and preprocess raw data by assessing quality, cleaning, and structuring it for modeling

• Develop and deploy machine learning models for regression, classification, clustering, forecasting, and recommendations

• Design scalable and accurate predictive algorithms using advanced ML techniques

• Collaborate with engineering teams to productionize ML models via APIs and Docker

• Build and maintain CI/CD pipelines and MLOps workflows for continuous delivery

• Monitor model performance, detect drift, and implement retraining strategies

• Generate actionable insights to improve business outcomes

• Qualifications

• Bachelor’s degree or equivalent experience in Statistics, Mathematics, Computer Science, Engineering, or related quantitative field

• 7–12 years of experience in Machine Learning / Data Science roles

• Strong understanding of predictive modeling, ML algorithms, and statistical techniques

• Proficiency in Python and SQL with hands-on experience in Num

Py, Pandas, and Scikit-learn

• Experience with ML algorithms such as XGBoost, Random Forest, SVM, Decision Trees, and ensemble methods

• Hands-on experience in ML model deployment, APIs, Docker, and CI/CD pipelines

• Familiarity with MLOps tools (MLflow / Kubeflow / Airflow) and cloud platforms (AWS / Azure / GCP)

• Knowledge of BI tools like Power BI or Tableau is a plus